[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4607":3,"related-4607":85},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":25,"secondary_topics":31,"classification":32,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":84},4607,"Topographic gradients shape territorial-space evolution and their nonlinear drivers in a karst mountain region of Southwest China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74236-6","Abstract Karst mountainous areas are characterised by fragmented terrain and high ecological sensitivity, which make territorial-space evolution strongly terrain dependent. However, existing studies often treat terrain merely as a general control variable, leaving the spatial transitions and nonlinear driving mechanisms across different terrain gradients insufficiently understood. Taking the karst mountainous area of northwestern Guangxi as a case study, this study integrates land-use, natural environmental, socioeconomic and locational data from 1980 to 2020. A transition matrix, terrain distribution index (TDI), and explainable machine-learning models were employed to identify the terrain-gradient effects and driving mechanisms of territorial-space evolution. The results show that vegetation-dominated ecological space (VES) consistently accounted for more than 80% of the study area. Nevertheless, the study period was accompanied by the continuous expansion of urban space (US), whose area increased by approximately 397.66% from 1980 to 2020, while ecological space (ES) and agricultural space (AS) declined overall. Transitions from AS to ES were mainly concentrated in middle- to high-elevation and relatively steep areas, whereas transitions from AS to US were clustered in low-elevation and gentle-slope areas. Aquatic ecological space (AES), US and AS tended to occur in low-elevation and gentle-slope areas, whereas VES was dominant in high-elevation and steep-slope areas. The random forest (RF) model performed best, with an R 2 of 0.791 on the test set, outperforming ordinary least squares (OLS) and extreme gradient boosting (XGBoost). Shapley additive explanations (SHAP) analysis based on the RF model further indicated that the normalised difference vegetation index (NDVI), population density (POP) and distance to rivers (DRIV) were the leading explanatory factors. The dominant roles of natural environmental, socioeconomic and locational accessibility factors shifted across elevation and slope gradients and exhibited clear threshold effects. These findings show how terrain gradients are associated with territorial-space patterns, transition pathways and driving mechanisms, and provide a scientific basis for territorial-space optimisation in karst mountainous areas.","喀斯特山区地形破碎、生态敏感性强，其国土空间演变具有强烈的地形依赖性。然而，现有研究多将地形视为一般性控制变量，对不同地形梯度下国土空间转换过程及非线性驱动机制的认识仍不充分。本研究以桂西北喀斯特山区为案例，整合1980—2020年土地利用、自然环境、社会经济和区位数据，采用转移矩阵、地形分布指数（TDI）和可解释机器学习模型，识别国土空间演变的地形梯度效应与驱动机制。结果表明：以植被为主的生态空间（VES）始终占研究区面积的80%以上；但研究期内城镇空间（US）持续扩张，1980—2020年面积增加约397.66%，而生态空间（ES）和农业空间（AS）总体下降。AS向ES的转换主要集中于中高海拔和较陡区域，而AS向US的转换则聚集在低海拔和缓坡区域。水域生态空间（AES）、US和AS倾向于分布在低海拔和缓坡区域，而VES则在高海拔和陡坡区域占主导。随机森林（RF）模型表现最佳，测试集R²为0.791，优于普通最小二乘法（OLS）和极端梯度提升（XGBoost）。基于RF模型的SHAP（Shapley加性解释）分析进一步表明，归一化植被指数（NDVI）、人口密度（POP）和距河流距离（DRIV）是主要解释因子。自然环境、社会经济和区位可达性因子的主导作用随海拔和坡度梯度发生转变，并表现出明显的阈值效应。上述发现揭示了地形梯度与国土空间格局、转换路径及驱动机制之间的关联，可为喀斯特山区国土空间优化提供科学依据。",null,"Scientific Reports","2026-10-09T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦喀斯特山区国土空间演化的地形梯度与非线性驱动机制，属土地利用与景观生态研究，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。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","土地利用","喀斯特山区","国土空间",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"classified","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":40},[38,45,51],{"code":26,"score":39,"supported":40,"title":41,"abstract":42,"tags":44},3,true,[],[43],"机器学习",[],{"code":46,"score":47,"supported":14,"title":48,"abstract":49,"tags":50},"H6",1,[],[],[22],{"code":52,"score":47,"supported":14,"title":53,"abstract":54,"tags":55},"I3",[],[],[21],[57,58],"桂西北 喀斯特 国土空间演化","喀斯特山区 国土空间 土地利用 遥感监测","桂西北喀斯特国土空间演化-4607",2,"10.1038\u002Fs41598-026-74236-6",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":15,"oa_url":6,"card":76,"direction":82,"ingested_from":83},"W7221135432",[65,67,69,71,73],{"name":66,"orcid":9},"Xiangying Kong",{"name":68,"orcid":9},"Baoqing Hu",{"name":70,"orcid":9},"Yurou Liang",{"name":72,"orcid":9},"Jiaxin Li",{"name":74,"orcid":75},"Xuan Zhong","https:\u002F\u002Forcid.org\u002F0009-0000-1258-4363",{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"分析桂西北喀斯特山区1980-2020年国土空间演变的地形梯度效应与非线性驱动机制。","土地利用转移矩阵、地形分布指数与可解释机器学习（随机森林+SHAP）。","城镇空间增约397.66%，农业转生态集中于中高海拔陡坡，NDVI、人口密度和距河距离为主导驱动且存","农业遥感与作物表型","可将地形梯度阈值与可解释机器学习结合，用于喀斯特山区国土空间优化与生态修复分区。","农业人工智能与决策模型","openalex","2026-10-10T23:30:50.166440Z",{"total":86,"page":47,"page_size":86,"items":87},6,[88,160,210,304,390,454],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":96,"sources":98,"tags":100,"primary_topic":102,"secondary_topics":103,"classification":109,"search_phrases":137,"slug":140,"view_count":15,"doi":141,"paper":142,"created_at":159},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":97},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[99],{"name":94,"url":91},[43,21,22,101],"城市扩张",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[104],{"code":105,"slug":106,"name":107,"path":108,"parent_code":30},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":33,"locked":14,"version":34,"method":35,"evidence":110},{"candidates":111,"agricultural_evidence":40},[112,118,126,133],{"code":26,"score":113,"supported":40,"title":114,"abstract":116,"tags":117},15,[115],"machine learning",[43,115],[43],{"code":105,"score":119,"supported":40,"title":120,"abstract":121,"tags":125},9,[],[122,123,124],"空间分析","GIS","spatial analysis",[],{"code":52,"score":127,"supported":40,"title":128,"abstract":129,"tags":132},7,[],[130,131],"遥感","remote sensing",[21],{"code":46,"score":47,"supported":14,"title":134,"abstract":135,"tags":136},[],[],[22],[138,139],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":141,"openalex_id":143,"authors":144,"venue":94,"cited_by_count":15,"oa_url":91,"card":154,"direction":80,"ingested_from":83},"W7223022155",[145,148,150,152],{"name":146,"orcid":147},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":149,"orcid":9},"Goanar Jal Dup",{"name":151,"orcid":9},"Weyesa Merga Chemeda",{"name":153,"orcid":9},"Shankar Karuppannan",{"tldr":155,"method":156,"finding":157,"direction":80,"opportunity":158},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":161,"title":162,"url":163,"summary":164,"summary_zh":165,"content":9,"source_name":166,"source_url":163,"published_at":167,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":168,"sources":170,"tags":172,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":192,"slug":195,"view_count":196,"doi":197,"paper":198,"created_at":209},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":169},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，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。",[171],{"name":166,"url":163},[173,21,22,101],"深度学习",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],{"status":33,"locked":14,"version":34,"method":35,"evidence":177},{"candidates":178,"agricultural_evidence":40},[179,184,188],{"code":26,"score":113,"supported":40,"title":180,"abstract":182,"tags":183},[181],"deep learning",[173,181],[173],{"code":52,"score":127,"supported":40,"title":185,"abstract":186,"tags":187},[],[130,131],[21],{"code":46,"score":47,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[22],[193,194],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015",4,"10.2478\u002Fjlecol-2026-0037",{"doi":197,"openalex_id":199,"authors":200,"venue":166,"cited_by_count":15,"oa_url":163,"card":204,"direction":80,"ingested_from":83},"W7213670913",[201],{"name":202,"orcid":203},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":205,"method":206,"finding":207,"direction":80,"opportunity":208},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":9,"source_name":216,"source_url":213,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":225,"tags":227,"primary_topic":231,"secondary_topics":237,"classification":245,"search_phrases":280,"slug":283,"view_count":15,"doi":284,"paper":285,"created_at":303},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":220,"substance":221,"depth":222,"authority":223,"freshness":13,"relevant":47,"comment":224},16,21,17,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[226],{"name":216,"url":213},[228,43,21,229,230],"智慧农业","土壤墒情","冬小麦",{"code":232,"slug":233,"name":234,"path":235,"parent_code":236},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[238,244],{"code":239,"slug":240,"name":241,"path":242,"parent_code":243},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":246},{"candidates":247,"agricultural_evidence":40},[248,254,259,263,268,275],{"code":232,"score":249,"supported":40,"title":250,"abstract":252,"tags":253},26,[251,233],"winter wheat",[234,251,233],[230],{"code":239,"score":113,"supported":40,"title":255,"abstract":256,"tags":258},[240],[257,240],"土壤",[229],{"code":26,"score":113,"supported":40,"title":260,"abstract":261,"tags":262},[115],[43,115],[43],{"code":52,"score":13,"supported":40,"title":264,"abstract":265,"tags":267},[],[130,131,266],"multispectral",[21],{"code":269,"score":86,"supported":40,"title":270,"abstract":271,"tags":274},"H3",[],[272,273],"灌溉","irrigation",[],{"code":276,"score":196,"supported":40,"title":277,"abstract":278,"tags":279},"I1",[],[228],[228],[281,282],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":284,"openalex_id":286,"authors":287,"venue":216,"cited_by_count":15,"oa_url":213,"card":297,"direction":302,"ingested_from":83},"W7223156204",[288,291,293,295],{"name":289,"orcid":290},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":292,"orcid":9},"Yi Zhang",{"name":294,"orcid":9},"WEI Lei",{"name":296,"orcid":9},"Chengcai Zhang",{"tldr":298,"method":299,"finding":300,"direction":80,"opportunity":301},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":309,"content":9,"source_name":310,"source_url":307,"published_at":311,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":324,"classification":330,"search_phrases":373,"slug":376,"view_count":47,"doi":377,"paper":378,"created_at":389},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":314,"substance":220,"depth":113,"authority":315,"freshness":119,"relevant":47,"comment":316},18,12,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[318],{"name":310,"url":307},[228,320,321,322,21],"农业人工智能","可持续发展","气候变化",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[325],{"code":326,"slug":327,"name":328,"path":329,"parent_code":243},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":33,"locked":14,"version":34,"method":35,"evidence":331},{"candidates":332,"agricultural_evidence":40},[333,338,343,349,353,360,366],{"code":26,"score":314,"supported":40,"title":334,"abstract":336,"tags":337},[335],"artificial intelligence",[173,43,115,181,335],[320],{"code":326,"score":113,"supported":40,"title":339,"abstract":341,"tags":342},[340],"climate change",[322,340],[322],{"code":276,"score":127,"supported":40,"title":344,"abstract":345,"tags":348},[],[346,347],"精准农业","precision agriculture",[228],{"code":52,"score":127,"supported":40,"title":350,"abstract":351,"tags":352},[],[130,131],[21],{"code":354,"score":86,"supported":40,"title":355,"abstract":356,"tags":359},"H4",[],[357,358],"碳排放","carbon emission",[],{"code":46,"score":86,"supported":40,"title":361,"abstract":362,"tags":365},[],[363,364],"生物多样性","biodiversity",[],{"code":367,"score":86,"supported":40,"title":368,"abstract":369,"tags":372},"I5",[],[370,371],"物联网","internet of things",[],[374,375],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":377,"openalex_id":379,"authors":380,"venue":310,"cited_by_count":15,"oa_url":307,"card":383,"direction":302,"ingested_from":83},"W7222092855",[381],{"name":382,"orcid":9},"Deepmala Suryavanshi",{"tldr":384,"method":385,"finding":386,"direction":387,"opportunity":388},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":399,"sources":403,"tags":405,"primary_topic":409,"secondary_topics":410,"classification":415,"search_phrases":428,"slug":431,"view_count":15,"doi":432,"paper":433,"created_at":453},4897,"Wild boar collision data and satellite computer vision refine habitat suitability mapping across France","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10344-026-02160-1","Abstract Wild boar (Sus scrofa) populations have expanded rapidly across Europe, leading to escalating human-wildlife conflicts (HWCs), notably wildlife-vehicle collisions (WVCs), increased agricultural damage and disease transmission. In continental France these issues are compounded by the species’ ecological adaptability exhibiting increasing overlaps with urbanization and transportation networks. In this study, we leverage time-series environmental data, computer vision and Species Distribution Modeling (SDM) to predict wild boar habitat suitability and investigate its spatiotemporal drivers. We use presence-only data from WVC reports on the national railway and road networks, along with publicly available GBIF observation collections, as complementary occurrence streams in our SDMs, while addressing inherent sampling biases of these datasets with tailored corrections. A key innovation of this study is the integration of national scale, annually updated high-resolution land cover predictors explicitly focused on wild boar resource preference. By fine-tuning a multitemporal Vision Transformer foundational AI model on multispectral satellite remote sensing imagery we capture subtle seasonal phenological differences. Our results highlight clear spatial, seasonal and annual variations in wild boar habitat suitability. The multitemporal SDM pipeline yields meaningful predictions when extrapolating to novel environmental scenarios such as climate extremes. Adding rail and road WVC records to GBIF-based SDMs retained high performance metrics while altering predicted spatial patterns and feature importance distribution. The methodological and ecological insights gained through this study provide additional knowledge for French transportation planning, agriculture and wildlife management. Identifying regions with high seasonal habitat suitability can inform targeted interventions. More broadly, our results demonstrate that advanced, data-driven methods are becoming indispensable for proactively and sustainably addressing HWCs in an increasingly anthropogenic world.","摘要 野猪（Sus scrofa）种群在欧洲迅速扩张，导致人兽冲突（HWCs）不断升级，尤其是野生动物车辆碰撞（WVCs）、农业损害加剧和疾病传播。在法国大陆，该物种的生态适应性使其与城市化和交通网络的重叠日益增加，进一步加剧了这些问题。本研究利用时间序列环境数据、计算机视觉和物种分布模型（SDM）预测野猪栖息地适宜性，并探究其时空驱动因素。我们使用国家铁路和公路网络中WVC报告的存在-only数据，以及公开可用的GBIF观测集合，作为SDM中的互补出现数据流，同时通过针对性校正处理这些数据集固有的采样偏差。本研究的一个关键创新是整合了国家尺度、年度更新的高分辨率土地覆盖预测因子，明确聚焦于野猪的资源偏好。通过在多光谱卫星遥感影像上微调多时相视觉Transformer基础AI模型，我们捕捉到了细微的季节物候差异。我们的结果突出了野猪栖息地适宜性在空间、季节和年际上的明显变化。多时相SDM流程在推断至气候极端等新型环境情景时产生了有意义的预测。将铁路和公路WVC记录添加到基于GBIF的SDM中，在保持高性能指标的同时改变了预测的空间格局和特征重要性分布。本研究获得的方法学和生态学见解为法国交通规划、农业和野生动物管理提供了额外知识。识别具有高季节性栖息地适宜性的区域可为针对性干预提供依据。更广泛地说，我们的结果表明，在日益受到人类影响的世界中，先进的数据驱动方法正成为前瞻性和可持续地应对人兽冲突不可或缺的手段。","European Journal of Wildlife Research","2026-10-13T00:00:00Z",81,{"impact":314,"substance":400,"depth":314,"authority":401,"freshness":119,"relevant":47,"comment":402},22,14,"该研究将卫星遥感与计算机视觉用于野猪栖息地建模，方法新颖、数据规模大，对交通规划与农业野生动物管理有参考价值，但属法国区域研究，国内产业影响有限。",[404],{"name":396,"url":393},[320,21,406,407,408],"栖息地评估","野生动物管理","人兽冲突",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[411],{"code":52,"slug":412,"name":413,"path":414,"parent_code":30},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":33,"locked":14,"version":34,"method":35,"evidence":416},{"candidates":417,"agricultural_evidence":40},[418,424],{"code":26,"score":113,"supported":40,"title":419,"abstract":421,"tags":423},[420],"computer vision",[422,420],"计算机视觉",[320],{"code":52,"score":13,"supported":40,"title":425,"abstract":426,"tags":427},[],[130,131,266],[21],[429,430],"法国 野猪 栖息地 遥感","野猪 车辆碰撞 卫星影像","法国野猪栖息地遥感-4897","10.1007\u002Fs10344-026-02160-1",{"doi":432,"openalex_id":434,"authors":435,"venue":396,"cited_by_count":15,"oa_url":393,"card":448,"direction":80,"ingested_from":83},"W7221702437",[436,439,441,444,446],{"name":437,"orcid":438},"Adrian Meyer","https:\u002F\u002Forcid.org\u002F0000-0002-8652-1477",{"name":440,"orcid":9},"Théo Reibel",{"name":442,"orcid":443},"Кevin Мorelle","https:\u002F\u002Forcid.org\u002F0000-0001-9041-6541",{"name":445,"orcid":9},"Mathias Kneubühler",{"name":447,"orcid":9},"Denis Jordan",{"tldr":449,"method":450,"finding":451,"direction":80,"opportunity":452},"融合野猪碰撞数据与卫星视觉模型，预测法国野猪栖息地适宜性及其时空驱动因素。","时间序列环境数据、Vision Transformer卫星影像、物种分布模型与G","栖息地适宜性呈明显时空变化，加入碰撞记录改变预测格局与特征重要性。","可将碰撞数据与遥感表型结合，拓展至其他野生动物-农业冲突的时空预警与决策。","2026-10-14T23:30:27.244673Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":474,"classification":476,"search_phrases":499,"slug":502,"view_count":39,"doi":503,"paper":504,"created_at":516},4809,"Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12083-026-02299-3","Abstract Precision agriculture is one of the major breakthroughs which has been adopted to counteract some of the major challenges facing contemporary agriculture, such as maximizing crop yield, maximizing resource efficiency, and sustainability. The application of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies can go a long way in enhancing crop yield prediction through data obtained from multiple sources, including satellite images, ground sensors, and weather stations. This research paper proposes a next-generation crop yield prediction system called Geo-Optimized Deep Learning (GODL) algorithm. The GODL algorithm integrates geospatial information with deep learning algorithms for crop yield analysis and prediction. This combination of deep learning and geospatial data allows accurate and scalable crop yield predictions using geospatial and sensor information. In order to ensure model optimization in terms of accuracy and efficiency, the GODL Algorithm makes use of advanced hyperparameter optimization methods that include Adam and Stochastic Gradient Descent (SGD). The research paper highlights the efficiency of the GODL Algorithm by conducting extensive experiments that compare the algorithm’s performance with other crop yield prediction models like Recurrent Neural Network (RNN) and other machine learning techniques. The evaluation takes into consideration important metrics such as accuracy, Jaccard Index, Fowlkes-Mallows Index (FMI), and G-mean, as well as IoT data delay and its effects on the prediction accuracy. In future research, the use of blockchain technology to facilitate data sharing and edge computing to process the data will be considered.","摘要 精准农业是为应对当代农业面临的若干重大挑战而采用的主要突破之一，这些挑战包括最大化作物产量、最大化资源效率以及可持续性。人工智能（AI）与物联网（IoT）技术的应用，能够通过从卫星图像、地面传感器和气象站等多源获取的数据，大幅提升作物产量预测能力。本文提出了一种下一代作物产量预测系统，称为地理优化深度学习（GODL）算法。GODL算法将地理空间信息与深度学习算法相结合，用于作物产量分析与预测。深度学习与地理空间数据的这种结合，使得利用地理空间和传感器信息进行准确且可扩展的作物产量预测成为可能。为确保模型在准确性和效率方面实现优化，GODL算法采用了先进的超参数优化方法，包括Adam和随机梯度下降（SGD）。本文通过开展大量实验，将该算法的性能与其他作物产量预测模型（如循环神经网络（RNN）及其他机器学习技术）进行比较，从而突出GODL算法的效率。评估考虑了准确率、Jaccard指数、Fowlkes-Mallows指数（FMI）和G均值等重要指标，以及物联网数据延迟及其对预测精度的影响。在未来的研究中，将考虑使用区块链技术促进数据共享，并利用边缘计算处理数据。","Peer-to-Peer Networking and Applications","2026-10-12T00:00:00Z",68,{"impact":315,"substance":314,"depth":220,"authority":223,"freshness":119,"relevant":47,"comment":464},"提出GODL地理优化深度学习算法融合AI与IoT进行作物估产，方法有创新且对比实验充分，但属学术论文，产业落地影响有限。",[466],{"name":460,"url":457},[228,320,468,21,469],"农业物联网","作物估产",{"code":367,"slug":471,"name":472,"path":473,"parent_code":30},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[475],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"status":33,"locked":14,"version":34,"method":35,"evidence":477},{"candidates":478,"agricultural_evidence":40},[479,486,491,495],{"code":367,"score":314,"supported":40,"title":480,"abstract":482,"tags":485},[481],"IoT",[370,483,371,481,484],"边缘计算","edge computing",[468],{"code":276,"score":113,"supported":40,"title":487,"abstract":489,"tags":490},[488],"smart agriculture",[346,347],[228],{"code":26,"score":13,"supported":40,"title":492,"abstract":493,"tags":494},[],[173,43,115,181,335],[320],{"code":52,"score":60,"supported":14,"title":496,"abstract":497,"tags":498},[],[],[469,21],[500,501],"GODL 作物估产 算法","AI IoT 智慧农业 产量预测","GODL作物估产算法-4809","10.1007\u002Fs12083-026-02299-3",{"doi":503,"openalex_id":505,"authors":506,"venue":460,"cited_by_count":15,"oa_url":457,"card":511,"direction":302,"ingested_from":83},"W7221560555",[507,509],{"name":508,"orcid":9},"Keerthana T",{"name":510,"orcid":9},"Janarthanan R",{"tldr":512,"method":513,"finding":514,"direction":302,"opportunity":515},"提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。","GODL算法，融合地理空间数据与深度学习，采用Adam和SGD超参数优化。","GODL在准确率、Jaccard指数、FMI和G-mean上优于RNN等模型，且IoT数据延迟影响预","可探索区块链保障数据共享安全、边缘计算降低IoT延迟，以及多源异构数据融合的轻量化模型。","2026-10-13T23:30:10.304344Z"]