[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3084":3,"related-3084":100},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":44,"search_phrases":63,"slug":66,"view_count":67,"doi":68,"paper":69,"created_at":99},3084,"Spatiotemporal evolution and drivers of eco-environmental quality for sustainable rural tourism in China’s Yangtze River Basin: an explainable AI approach","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2735566","Balancing tourism-driven growth (SDG 8.9) with terrestrial ecosystem conservation (SDG 15) is vital for sustainable watershed management. Focusing on 409 Key Rural Tourism Villages in China’s Yangtze River Basin, this study integrates Google Earth Engine and MODIS data to assess eco-environmental quality (EEQ) from 2000 to 2024 using the Remote Sensing Ecological Index (RSEI), employing an XGBoost-SHAP framework to decode driving mechanisms. Results indicate that these villages exhibit a clustered spatial distribution pattern, with three core agglomerations. The mean RSEI across all villages exceeds 0.73, indicating overall improvement and resilient recovery after 2020. However, a distinct north-south spatial disparity emerged: improved villages were predominantly concentrated north of the Yangtze River mainstem, whereas degraded villages clustered in the Taihu, Poyang, and Dongting Lake basins to the south. Elevation, land-use change, and temperature are the primary drivers, with a critical altitudinal threshold near 2,500 metres, where synergistic effects turn antagonistic. Although the XGBoost model achieved strong predictive performance (R2 = 0.853, RMSE = 0.071), 14.7% of the variance remains unexplained, suggesting the influence of additional unmeasured factors. These findings advance SDG 15 localisation by establishing spatial baselines and threshold-guided carrying-capacity regulations for watershed tourism governance.","在可持续流域管理中，平衡旅游驱动型增长（SDG 8.9）与陆地生态系统保护（SDG 15）至关重要。本研究以中国长江流域409个全国乡村旅游重点村为对象，集成Google Earth Engine与MODIS数据，基于遥感生态指数（RSEI）评估2000—2024年生态环境质量（EEQ），并采用XGBoost-SHAP框架解析其驱动机制。结果表明，这些村庄呈集聚型空间分布格局，形成三个核心集聚区。所有村庄的RSEI均值超过0.73，表明生态环境质量总体改善，并在2020年后呈现韧性恢复。然而，南北空间分异显著：改善型村庄主要集中于长江干流以北，而退化型村庄则聚集于以南的太湖、鄱阳湖和洞庭湖流域。海拔、土地利用变化和气温是主要驱动因子，在约2500 m处存在关键海拔阈值，超过该阈值后协同效应转为拮抗效应。尽管XGBoost模型具有较强预测性能（R² = 0.853，RMSE = 0.071），仍有14.7%的方差未能解释，提示其他未测因素的影响。本研究通过建立空间基线和阈值引导的承载力调控措施，推动了SDG 15的本土化，为流域旅游治理提供了依据。",null,"International Journal of Digital Earth","2026-09-18T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"基于GEE与MODIS的乡村文旅生态质量长时序评估，方法新颖、数据规模大，对流域旅游承载力治理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"长江流域","遥感监测","乡村文旅","生态遥感","可持续旅游",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"G4","rural-industry","乡村产业与县域经济","\u002Ftopic\u002Frural-services\u002Frural-industry","G",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,57],{"code":32,"score":19,"supported":51,"title":52,"abstract":54,"tags":56},true,[53],"rural tourism",[55,53],"乡村旅游",[],{"code":39,"score":20,"supported":51,"title":58,"abstract":59,"tags":62},[],[60,61],"遥感","remote sensing",[29,27],[64,65],"长江流域 乡村旅游 生态质量","RSEI 遥感生态指数 乡村","长江流域乡村旅游生态质量-3084",5,"10.1080\u002F17538947.2026.2735566",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":89,"oa_url":90,"card":91,"direction":97,"ingested_from":98},"W7213546376",[72,74,77,79,81,83,86],{"name":73,"orcid":9},"Xiaojuan Zhang",{"name":75,"orcid":76},"Zhe Chen","https:\u002F\u002Forcid.org\u002F0000-0001-7579-1968",{"name":78,"orcid":9},"Libin Guo",{"name":80,"orcid":9},"Guoyan Wang",{"name":82,"orcid":9},"Yongxiu Zhou",{"name":84,"orcid":85},"Hui Li","https:\u002F\u002Forcid.org\u002F0000-0002-3565-1773",{"name":87,"orcid":88},"Zhongchang Sun","https:\u002F\u002Forcid.org\u002F0000-0003-3219-0542",0,"https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2735566?needAccess=true",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"评估长江流域409个乡村旅游村2000-2024年生态环境质量演变并解析驱动机制。","GEE与MODIS数据、RSEI指数、XGBoost-SHAP可解释AI框架。","整体EEQ改善但南北分异明显，海拔、土地利用与温度为主导驱动，2500米为协同转拮抗阈值。","农业绿色发展与碳","可探究未解释的14.7%方差来源，并针对太湖、鄱阳湖、洞庭湖退化区开展旅游承载力调控研究。","数字乡村与农业信息化","openalex","2026-09-21T23:30:36.842989Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,186,262,343,405,473],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":123,"classification":124,"search_phrases":167,"slug":170,"view_count":171,"doi":172,"paper":173,"created_at":185},3894,"Assessment of Spatio-temporal Dynamics of the Remote Sensing Ecological Index (RSEI) based on Landsat 8 Data: A Case Study of Kherson Region, Ukraine (2013–2025)","https:\u002F\u002Fdoi.org\u002F10.36023\u002Fujrs.2026.13.3.310","This scientific paper presents a comprehensive assessment of the spatio-temporal dynamics of the ecological state of the Kherson region from 2013 to 2025, with a particular focus on the consequences of the full-scale military aggression by the Russian Federation. Amidst intensive hostilities, the destruction of critical infrastructure, and global climate challenges, objective geospatial monitoring of the natural environment is a crucial task for documenting damages and forecasting the region's future development.The study is based on the use of modern geographic information systems and remote sensing data. Using the Google Earth Engine cloud computing platform and an archive of multispectral satellite imagery from Landsat 8 OLI\u002FTIRS, the Remote Sensing Ecological Index (RSEI) was calculated. This index comprehensively integrates four key biophysical indicators: greenness (based on the Normalized Difference Vegetation Index, NDVI), wetness (Wet), dryness (based on the Normalized Difference Build-up and Soil Index, NDBSI), and land surface temperature (LST). The application of Principal Component Analysis (PCA) allowed these indicators to be mathematically combined into a single standardized index, where the first principal component (PC1) explains the highest proportion of the total spatial data variance. This indicates that the first principal component (PC1) successfully captures the vast majority of the variance among the four input indicators, which is a basic premise of the RSEI methodology.The results of the cartographic and statistical analyses revealed a sharp, unprecedented deterioration in the ecosystem health of the study region in 2025 compared to retrospective data from previous years. A critical drop in the average RSEI value was established – from 0.381 in the relatively stable year of 2021 to 0.205 in 2025. The spatial assessment demonstrated that the area of territories classified as being in a state of ecological crisis (the «Terrible» category, with an index ranking from 0 to 0.2) more than doubled: from 26% of the region's total area in 2021 to a critical 60% in 2025.The sharp drop in RSEI spatially aligns with the large-scale hydrological transformations caused by the destruction of the Kakhovka Dam; however, this indicator also reflects the synergistic impact of intense hostilities, fires, the cessation of irrigation, and climate anomalies. Kakhovka Dam destruction led to the catastrophic destruction of the hydrographic network, the disappearance of water supply sources for vital agricultural lands, and the disruption of life-support systems in urbanized areas. The only exception against the general backdrop of the ecological catastrophe is the recorded local natural restoration of the old Dnipro riverbed ecosystem, which is gradually returning to its historical state that preceded massive anthropogenic intervention and river regulation in the 1950s.In conclusion, the synergistic impact of continuous large-scale hostilities, the destruction of established ecosystem links, and the exacerbation of global climate change shape an extremely bleak forecast. Further degradation of the soil and vegetation cover and the depletion of water resources create a high risk of transforming a significant part of the Kherson region into a territory unfit for safe living, agriculture, and socio-economic functioning. Disclosure of AI use. In the process of conducting research and preparing the manuscript, the author used the large language model Google Gemini 3 Pro. The tool acted as a technical assistant and consultant at the stages:1. Software development: assistance in writing, debugging and optimizing JavaScript scripts for the Google Earth Engine environment, in particular the implementation of the PCA algorithm.2. Text preparation: providing recommendations on structuring the work, describing the methodology and improving the academic style of presentation.The author carried out a full check of the generated code and text, verified the results obtained and bears sole responsibility for the content of this publication. Data availability and integrity. The JavaScript code for Google Earth Engine developed for this study has been deposited in the Zenodo repository with a reserved DOI to ensure long-term accessibility and reproducibility of the results. The code will be publicly available upon acceptance of the manuscript for publication. The data can be provided by the author upon reasonable request. Conflicts of Interest. The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements. The author is grateful to the reviewers and editors for their valuable comments, recommendations, and attention to the work.","本文对2013年至2025年赫尔松地区生态状况的时空动态进行了综合评估，尤其关注俄罗斯联邦全面军事侵略所造成的后果。在激烈的敌对行动、关键基础设施的破坏以及全球气候挑战的背景下，对自然环境进行客观的地理空间监测是记录损害和预测该地区未来发展的关键任务。本研究基于现代地理信息系统和遥感数据的应用。利用Google Earth Engine云计算平台和Landsat 8 OLI\u002FTIRS多光谱卫星影像档案，计算了遥感生态指数（RSEI）。该指数综合集成了四个关键生物物理指标：绿度（基于归一化差异植被指数，NDVI）、湿度（Wet）、干度（基于归一化差异建筑与土壤指数，NDBSI）以及地表温度（LST）。主成分分析（PCA）的应用使这些指标能够在数学上组合为一个标准化的单一指数，其中第一主成分（PC1）解释了总空间数据方差的最大比例。这表明第一主成分（PC1）成功捕捉了四个输入指标之间的绝大部分方差，这是RSEI方法论的基本前提。制图与统计分析的结果揭示，与往年回顾性数据相比，2025年研究区域的生态系统健康出现了急剧且前所未有的恶化。研究确定了平均RSEI值的临界下降——从相对稳定的2021年的0.381降至2025年的0.205。空间评估表明，被归类为生态危机状态的领土面积（“极差”类别，指数排名从0到0.2）增加了一倍以上：从2021年占该地区总面积的26%增至2025年临界水平的60%。RSEI的急剧下降在空间上与卡霍夫卡大坝被毁所引发的大规模水文变化相吻合；然而，该指标也反映了激烈敌对行动、火灾、灌溉停止以及气候异常的协同影响。卡霍夫卡大坝被毁导致水文网络的灾难性破坏，","Ukrainian journal of remote sensing","2026-09-30T00:00:00Z",73,{"impact":113,"substance":18,"depth":17,"authority":113,"freshness":114,"relevant":21,"comment":115},12,9,"基于Landsat 8与Google Earth Engine的RSEI时空动态研究，方法规范、数据翔实，对战争与气候双重压力下的农业生态退化具有实证价值，但属区域案例、与国内三农信息化关联偏弱。",[117],{"name":109,"url":106},[119,27,29,120,121],"NDVI","Landsat 8","乌克兰农业",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":125},{"candidates":126,"agricultural_evidence":51},[127,133,140,147,154,161],{"code":39,"score":128,"supported":51,"title":129,"abstract":130,"tags":132},20,[61],[60,61,131],"multispectral",[119,29,27],{"code":134,"score":101,"supported":51,"title":135,"abstract":136,"tags":139},"H2",[],[137,138],"土壤","soil",[],{"code":141,"score":101,"supported":51,"title":142,"abstract":143,"tags":146},"H3",[],[144,145],"灌溉","irrigation",[],{"code":148,"score":101,"supported":51,"title":149,"abstract":150,"tags":153},"I4",[],[151,152],"地理信息","geographic information",[],{"code":155,"score":156,"supported":51,"title":157,"abstract":158,"tags":160},"H7",3,[],[159],"climate change",[],{"code":162,"score":156,"supported":51,"title":163,"abstract":164,"tags":166},"I2",[],[165],"large language model",[],[168,169],"Kakhovka Dam 生态遥感","乌克兰农业 生态遥感 遥感监测 Landsat 8","KakhovkaDam生态遥感-3894",2,"10.36023\u002Fujrs.2026.13.3.310",{"doi":172,"openalex_id":174,"authors":175,"venue":109,"cited_by_count":89,"oa_url":106,"card":179,"direction":183,"ingested_from":98},"W7214889788",[176],{"name":177,"orcid":178},"Maksym Tymchyshyn","https:\u002F\u002Forcid.org\u002F0000-0003-2966-4794",{"tldr":180,"method":181,"finding":182,"direction":183,"opportunity":184},"基于Landsat 8与GEE计算RSEI，评估乌克兰赫尔松地区2013–2025年生态时空变化。","Google Earth Engine + Landsat 8 OLI\u002FTIRS","2025年RSEI均值由2021年0.381降至0.205，生态危机区由26%扩至60%。","农业遥感与作物表型","可将RSEI框架迁移至战乱或灌溉中断农区，结合作物物候验证生态退化对农业生产的传导。","2026-10-01T23:30:28.551437Z",{"id":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":9,"source_name":192,"source_url":189,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":89,"score_detail":194,"sources":196,"tags":198,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":228,"slug":231,"view_count":89,"doi":232,"paper":233,"created_at":261},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":89,"substance":89,"depth":89,"authority":89,"freshness":89,"relevant":89,"comment":195},"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。",[197],{"name":192,"url":189},[27,199,200,201],"土地利用","土壤侵蚀","乡村人口",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":205},{"candidates":206,"agricultural_evidence":51},[207,212,217,223],{"code":39,"score":208,"supported":51,"title":209,"abstract":210,"tags":211},7,[],[60,61],[27],{"code":148,"score":156,"supported":51,"title":213,"abstract":214,"tags":216},[],[215],"GIS",[],{"code":218,"score":156,"supported":51,"title":219,"abstract":220,"tags":222},"I7",[],[221],"methodological framework",[],{"code":224,"score":21,"supported":14,"title":225,"abstract":226,"tags":227},"H6",[],[],[199],[229,230],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":232,"openalex_id":234,"authors":235,"venue":192,"cited_by_count":89,"oa_url":189,"card":255,"direction":183,"ingested_from":98},"W7222945959",[236,239,242,244,247,250,253],{"name":237,"orcid":238},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":240,"orcid":241},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":243,"orcid":9},"Милан Радовић",{"name":245,"orcid":246},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":248,"orcid":249},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":251,"orcid":252},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":254,"orcid":9},"Manojlović Stefan",{"tldr":256,"method":257,"finding":258,"direction":259,"opportunity":260},"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":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":268,"source_url":265,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":270,"score_detail":271,"sources":275,"tags":277,"primary_topic":282,"secondary_topics":288,"classification":290,"search_phrases":314,"slug":317,"view_count":89,"doi":318,"paper":319,"created_at":342},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":272,"substance":18,"depth":17,"authority":273,"freshness":20,"relevant":21,"comment":274},16,13,"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。",[276],{"name":268,"url":265},[278,279,27,280,281],"智慧农业","无人机","冬小麦","生物量估算",{"code":283,"slug":284,"name":285,"path":286,"parent_code":287},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[289],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":291},{"candidates":292,"agricultural_evidence":51},[293,299,303,309],{"code":283,"score":294,"supported":51,"title":295,"abstract":297,"tags":298},26,[296,284],"winter wheat",[285,296,284],[280],{"code":39,"score":272,"supported":51,"title":300,"abstract":301,"tags":302},[61],[60,61],[281,27],{"code":162,"score":101,"supported":51,"title":304,"abstract":305,"tags":308},[],[306,307],"机器学习","machine learning",[],{"code":310,"score":21,"supported":14,"title":311,"abstract":312,"tags":313},"I1",[],[],[278],[315,316],"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":318,"openalex_id":320,"authors":321,"venue":268,"cited_by_count":89,"oa_url":265,"card":337,"direction":183,"ingested_from":98},"W7222806066",[322,324,326,328,331,333,335],{"name":323,"orcid":9},"Guangtao Wang",{"name":325,"orcid":9},"Sumei Yao",{"name":327,"orcid":9},"Yanzheng Xie",{"name":329,"orcid":330},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":332,"orcid":9},"Xiangdong Chen",{"name":334,"orcid":9},"Yuhao Zhang",{"name":336,"orcid":9},"Lina Jiang",{"tldr":338,"method":339,"finding":340,"direction":183,"opportunity":341},"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":344,"title":345,"url":346,"summary":347,"summary_zh":348,"content":9,"source_name":349,"source_url":346,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":89,"score_detail":350,"sources":352,"tags":354,"primary_topic":358,"secondary_topics":359,"classification":369,"search_phrases":387,"slug":390,"view_count":89,"doi":391,"paper":392,"created_at":404},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":89,"substance":89,"depth":89,"authority":89,"freshness":89,"relevant":89,"comment":351},"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。",[353],{"name":349,"url":346},[355,27,356,357],"防灾减灾","洪水风险评估","GIS应用",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[360,364],{"code":148,"slug":361,"name":362,"path":363,"parent_code":43},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":155,"slug":365,"name":366,"path":367,"parent_code":368},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":370},{"candidates":371,"agricultural_evidence":51},[372,377,381],{"code":39,"score":373,"supported":51,"title":374,"abstract":375,"tags":376},15,[61],[60,61],[27],{"code":148,"score":19,"supported":51,"title":378,"abstract":379,"tags":380},[215],[151,215],[],{"code":155,"score":20,"supported":51,"title":382,"abstract":383,"tags":386},[],[384,385],"干旱","drought",[356,355],[388,389],"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":391,"openalex_id":393,"authors":394,"venue":349,"cited_by_count":89,"oa_url":398,"card":399,"direction":183,"ingested_from":98},"W7222885988",[395],{"name":396,"orcid":397},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":400,"method":401,"finding":402,"direction":183,"opportunity":403},"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":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":9,"source_name":411,"source_url":408,"published_at":193,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":412,"score_detail":413,"sources":415,"tags":417,"primary_topic":422,"secondary_topics":423,"classification":424,"search_phrases":436,"slug":439,"view_count":89,"doi":440,"paper":441,"created_at":472},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":18,"substance":128,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":414},"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。",[416],{"name":411,"url":408},[418,27,419,420,421],"农业政策","卫星遥感","秸秆焚烧","环保督察",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":425},{"candidates":426,"agricultural_evidence":51},[427,431],{"code":39,"score":272,"supported":51,"title":428,"abstract":429,"tags":430},[61],[60,61],[419,27],{"code":432,"score":21,"supported":14,"title":433,"abstract":434,"tags":435},"H5",[],[],[420],[437,438],"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":440,"openalex_id":442,"authors":443,"venue":411,"cited_by_count":89,"oa_url":9,"card":467,"direction":183,"ingested_from":98},"W7222877438",[444,446,448,450,452,454,456,459,461,463,465],{"name":445,"orcid":9},"Lu Dong",{"name":447,"orcid":9},"Linru Zhang",{"name":449,"orcid":9},"Na Liu",{"name":451,"orcid":9},"Xin Long",{"name":453,"orcid":9},"Ziyi Wang",{"name":455,"orcid":9},"Siyue Yang",{"name":457,"orcid":458},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":460,"orcid":9},"Qiyuan Wang",{"name":462,"orcid":9},"Zhanfeng Dong",{"name":464,"orcid":9},"Long Wu",{"name":466,"orcid":9},"Yichen Wang",{"tldr":468,"method":469,"finding":470,"direction":95,"opportunity":471},"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。","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":474,"title":475,"url":476,"summary":477,"summary_zh":478,"content":9,"source_name":479,"source_url":476,"published_at":269,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":89,"score_detail":480,"sources":482,"tags":484,"primary_topic":488,"secondary_topics":489,"classification":490,"search_phrases":497,"slug":500,"view_count":89,"doi":501,"paper":502,"created_at":517},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":89,"substance":89,"depth":89,"authority":89,"freshness":89,"relevant":89,"comment":481},"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。",[483],{"name":479,"url":476},[27,485,486,487],"地表温度","城市热岛","MODIS",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":491},{"candidates":492,"agricultural_evidence":51},[493],{"code":39,"score":20,"supported":51,"title":494,"abstract":495,"tags":496},[],[60,61],[485,27],[498,499],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":501,"openalex_id":503,"authors":504,"venue":479,"cited_by_count":89,"oa_url":476,"card":512,"direction":183,"ingested_from":98},"W7222794736",[505,508,510],{"name":506,"orcid":507},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":509,"orcid":9},"Shubham Mor",{"name":511,"orcid":9},"Gaurav Kalotra",{"tldr":513,"method":514,"finding":515,"direction":183,"opportunity":516},"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"]