[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1771":3,"related-1771":88},{"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":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":87},1771,"Google Earth Engine-Based Random Forest Classification of Land Use\u002FLand Cover Change in Mojolaban, Sukoharjo","https:\u002F\u002Fdoi.org\u002F10.59784\u002Fglosains.v7i3.865","Background: Mojolaban Subdistrict is a peri-urban area on the outskirts of Surakarta City that experiences continuous pressure from land conversion due to settlement expansion and the growth of non-agricultural activities. Conventional land-use monitoring is time-consuming and costly, necessitating a more efficient approach through cloud-based remote sensing technology. Objective: This study aims to (1) identify land-use\u002Fland-cover (LULC) changes in Mojolaban Subdistrict in 2004, 2014, and 2024 using Google Earth Engine (GEE), and (2) evaluate the accuracy of the classification results as a basis for assessing the feasibility of the method in supporting regional development analysis. Methods: A quantitative spatial method employing a descriptive-quantitative remote sensing approach was applied. Landsat 7 Enhanced Thematic Mapper Plus (ETM+) imagery (2004), Landsat 8 Operational Land Imager\u002FThermal Infrared Sensor (OLI\u002FTIRS) imagery (2014), and Sentinel-2 imagery (2024) were classified using the Random Forest algorithm on the Google Earth Engine (GEE) platform, followed by post-classification change detection. Results: The results show overall accuracies of 88.30% (κ = 0.850) for 2004, 90.22% (κ = 0.864) for 2014, and 86.18% (κ = 0.818) for 2024, all of which are categorized as almost perfect agreement. The classification reveals a consistent increase in built-up land from 2004 to 2024, particularly in the central and western parts of the subdistrict, accompanied by a decline in open land and increasing fragmentation of agricultural land in the eastern and southern parts. Conclusion: This study demonstrates the robustness and reliability of Google Earth Engine and the Random Forest classification algorithm for multitemporal land-use\u002Fland-cover (LULC) monitoring in peri-urban areas, as evidenced by the consistently high classification accuracies (86.18%–90.22%; κ = 0.818–0.864) achieved across the three observation years.","背景：Mojolaban街道是苏拉卡尔塔市郊的一个城乡过渡区，由于居住区扩张和非农业活动的增长，该地区持续面临土地转换的压力。传统的土地利用监测耗时且成本高昂，因此有必要通过基于云计算的遥感技术寻求更高效的方法。目的：本研究旨在（1）利用谷歌地球引擎（GEE）识别Mojolaban街道2004年、2014年和2024年的土地利用\u002F土地覆盖（LULC）变化；（2）评估分类结果的准确性，以此作为评价该方法在支持区域发展分析中可行性的基础。方法：采用定量空间方法，结合描述性定量遥感手段。分别使用Landsat 7增强型专题制图仪增强型（ETM+）影像（2004年）、Landsat 8陆地成像仪\u002F热红外传感器（OLI\u002FTIRS）影像（2014年）和Sentinel-2影像（2024年），在谷歌地球引擎（GEE）平台上采用随机森林算法进行分类，随后进行分类后变化检测。结果：结果显示，2004年总体精度为88.30%（κ=0.850），2014年为90.22%（κ=0.864），2024年为86.18%（κ=0.818），均属于几乎完全一致等级。分类结果揭示，从2004年到2024年，建设用地持续增加，尤其在街道中部和西部地区，同时伴随着开放土地的减少以及东部和南部农业用地日益碎片化。结论：本研究证明了谷歌地球引擎和随机森林分类算法在城乡过渡区多时相土地利用\u002F土地覆盖（LULC）监测中的稳健性和可靠性，三个观测年份均获得了一致的高分类精度（86.18%–90.22%；κ=0.818–0.864）作为有力证据。",null,"Glosains Jurnal Sains Global Indonesia","2026-09-05T00:00:00Z","论文",10,false,52,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,6,4,1,"研究利用GEE和随机森林算法监测城乡结合部土地利用变化，精度高，方法可靠，对农业信息化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业遥感","随机森林","土地利用","Google Earth Engine","城乡结合部",{"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":46},[45,52,57],{"code":33,"score":17,"supported":46,"title":47,"abstract":48,"tags":51},true,[],[49,50],"遥感","remote sensing",[30,27],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"H6",[],[],[29],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I2",[],[],[28],[63,64],"城乡结合部 农业遥感 土地利用 随机森林","城乡结合部 农业遥感","城乡结合部农业遥感土地利用随机森林-1771",2,"10.59784\u002Fglosains.v7i3.865",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":79,"oa_url":6,"card":80,"direction":84,"ingested_from":86},"W7208836989",[71,73,75,77],{"name":72,"orcid":9},"Bagaskara Fajareto Kusuma Wardana",{"name":74,"orcid":9},"Rita Noviani",{"name":76,"orcid":9},"Pipit Wijayanti",{"name":78,"orcid":9},"Chatarina Muryani",0,{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"利用GEE和随机森林分类，监测2004-2024年城乡过渡区土地利用变化，精度高。","GEE平台，Landsat和Sentinel-2影像，随机森林分类，后分类变化检","建设用地持续增加，农地破碎化，分类精度86-90%，方法可靠。","农业遥感与作物表型","可延伸至农业用地流失预警与城市扩张对农地影响的精细评估，结合高分辨率影像和时序分析。","openalex","2026-09-06T23:30:27.575667Z",{"total":20,"page":22,"page_size":20,"items":89},[90,167,241,340,402,463],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":149,"slug":152,"view_count":140,"doi":153,"paper":154,"created_at":166},3846,"Integrating Earth Observation Data for Assessing the Impact of Mining Ponds on Land Use and Land Cover Change","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.11.no10.2025.pg136.153","This study examines the relationship between mining ponds and land use and land cover (LULC) dynamics, emphasizing both the environmental challenges and the socio-economic opportunities they present. Earth observation data from Landsat and questionnaire administration were used to evaluate the spatial and socio-environmental impacts of mining activities in Jos South Local Government Area (LGA). Supervised Maximum Likelihood Classification was applied to multitemporal satellite imagery to determine LULC changes, while nearest neighborhood analysis assessed the spatial distribution of mining ponds. A stratified random sampling method was used to collect responses from five districts Du, Zawan, Kuru, Vwang, and Gyel. Results revealed that mining ponds accounted for approximately 2.03% (10.20 km²) of the total land area in 1990, showing a slight decline to 2.00% (10.04 km²) by 2021. Despite their negative impacts, such as land degradation, erosion, and pollution, mining ponds also contribute positively to local livelihoods. An all-year-round supply of water by these ponds makes activities such as irrigation farming, fish farming, block making, recreation and livestock husbandry possible at all times. These benefits generate a wide range of opportunities, which attract investments from private companies, all channeled towards the socio-economic development of Jos-south. We recommended prioritizing the adoption of modern technologies such as GIS and remote sensing by the government for consistent supervision of the expansion of these mining ponds, towards sustainable environmental development of the Jos-South local government area.","本研究考察了矿坑与土地利用和土地覆盖（LULC）动态之间的关系，重点关注其带来的环境挑战与社会经济机遇。研究利用Landsat地球观测数据和问卷调查，评估了乔斯南部地方政府辖区（LGA）采矿活动的空间及社会环境的影响。研究对多时相卫星影像采用监督最大似然分类法以确定土地利用和土地覆盖变化，同时运用最近邻分析评估矿坑的空间分布。采用分层随机抽样方法，从Du、Zawan、Kuru、Vwang和Gyel五个区收集了调查问卷。结果显示，1990年矿坑约占土地总面积的2.03%（10.20 km²），到2021年略微下降至2.00%（10.04 km²）。尽管矿坑带来了土地退化、侵蚀和污染等负面影响，但也对当地生计作出了积极贡献。这些矿坑全年供水，使得灌溉农业、养鱼、制砖、休闲娱乐和畜牧等活动得以常年开展。这些效益产生了广泛的机遇，吸引了私营企业的投资，全部用于乔斯南部的社会经济发展。我们建议政府优先采用地理信息系统（GIS）和遥感等现代技术，对这些矿坑的扩张进行持续监管，以促进乔斯南部地方政府辖区的环境可持续发展。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-28T00:00:00Z",60,{"impact":17,"substance":18,"depth":100,"authority":13,"freshness":101,"relevant":22,"comment":102},15,9,"基于Landsat遥感与问卷评估尼日利亚矿区池塘对土地利用变化的影响，方法规范但属区域案例，对国内三农信息化参考价值有限。",[104],{"name":96,"url":93},[27,106,29,107],"遥感监测","矿区生态",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":111},{"candidates":112,"agricultural_evidence":46},[113,117,124,131,138,145],{"code":33,"score":17,"supported":46,"title":114,"abstract":115,"tags":116},[],[49,50],[27,106],{"code":118,"score":20,"supported":46,"title":119,"abstract":120,"tags":123},"C",[],[121,122],"畜牧","livestock",[],{"code":125,"score":20,"supported":46,"title":126,"abstract":127,"tags":130},"H3",[],[128,129],"灌溉","irrigation",[],{"code":132,"score":20,"supported":46,"title":133,"abstract":134,"tags":137},"I4",[],[135,136],"地理信息","GIS",[],{"code":139,"score":140,"supported":46,"title":141,"abstract":142,"tags":144},"D",3,[],[143],"fish farming",[],{"code":53,"score":66,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[29,107],[150,151],"Jos South 矿区 土地利用","Landsat 遥感 土地覆盖","JosSouth矿区土地利用-3846","10.56201\u002Fijgem.vol.11.no10.2025.pg136.153",{"doi":153,"openalex_id":155,"authors":156,"venue":96,"cited_by_count":79,"oa_url":160,"card":161,"direction":84,"ingested_from":86},"W7214571622",[157],{"name":158,"orcid":159},"Momoh Khalifa Adeiza","https:\u002F\u002Forcid.org\u002F0009-0000-8594-7372","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 11 NO. 10 2025\u002FIntegrating Earth Observation 136-153.pdf",{"tldr":162,"method":163,"finding":164,"direction":84,"opportunity":165},"用Landsat影像与问卷评估矿区池塘对土地利用变化及社会经济的影响。","Landsat多时相影像、最大似然分类、最近邻分析与分层随机抽样问卷。","矿塘面积占比由1990年2.03%微降至2021年2.00%，但带来退化与生计双重效应。","可结合时序遥感与农户调查，量化矿塘对灌溉农业与生计的长期耦合影响。","2026-09-30T23:30:29.520262Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":174,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":180,"tags":182,"primary_topic":185,"secondary_topics":186,"classification":191,"search_phrases":221,"slug":224,"view_count":225,"doi":226,"paper":227,"created_at":240},3187,"Spatio-temporal analysis of Land Use and Land Cover (LULC) change using high resolution satellite data: A case study of Nagaon, Assam","https:\u002F\u002Fdoi.org\u002F10.31018\u002Fjans.v18i3.7774","Geographic information systems (GIS) coupled with satellite remote sensing (RS) have had a significant impact on the assessment and mapping of land surface dynamics, specifically the analysis of land-use land-cover (LULC) change. This study uses high-resolution multispectral LISS-IV (Linear Imaging Self-Scanning Sensor-IV) imagery with a 5.8-meter resolution to evaluate LULC trends in the Nagaon district of Assam between 2015 and 2024. Land use classes in a variety of categories, including vegetation, water bodies, agricultural land, built-up areas, scrubland, sandbars, tea plantations, and trees outside forests (TOF), were identified applying the maximum likelihood classification algorithm. Overall, The accuracy was 85.7% in 2015 and 90.3% in 2024, with respective Kappa Coefficients of 0.836 and 0.889, respectively. The results showed that between 2015 and 2024, the areas of tea plantations, natural vegetation, scrubland, and agricultural land fell by 0.26%, 6.14%, 0.26%, and 9.83%, respectively. Conversely, the waterbody, sandbar, built-up, and TOF have all increased by 0.29%, 0.91%, 0.82%, and 14.46%, respectively. This noticeable shift from conventional agricultural and natural vegetation landscapes toward tree-based systems and urban expansion on the study area, emphasize on matters concerning climate stress, declining soil fertility, economic benefits, policy support, and the need for resilient livelihoods.","地理信息系统（GIS）与卫星遥感（RS）相结合，对地表动态的评估与制图产生了显著影响，尤其是土地利用与土地覆盖（LULC）变化分析。本研究利用分辨率为5.8米的高分辨率多光谱LISS-IV（线性成像自扫描传感器-IV）影像，评估了阿萨姆邦纳冈县2015年至2024年间的LULC变化趋势。采用最大似然分类算法，识别了植被、水体、农地、建设用地、灌丛地、沙洲、茶园及林外树木（TOF）等多种土地利用类别。总体而言，2015年分类精度为85.7%，2024年为90.3%，Kappa系数分别为0.836和0.889。结果表明，2015年至2024年间，茶园、自然植被、灌丛地和农地面积分别减少了0.26%、6.14%、0.26%和9.83%。相反，水体、沙洲、建设用地和TOF分别增加了0.29%、0.91%、0.82%和14.46%。研究区从传统农业和自然植被景观向林基系统和城市扩张的显著转变，凸显了气候胁迫、土壤肥力下降、经济效益、政策支持以及韧性生计需求等相关问题。","Journal of Applied and Natural Science","https:\u002F\u002Fjournals.ansfoundation.org\u002Findex.php\u002Fjans\u002Farticle\u002Fview\u002F7774","2026-09-20T00:00:00Z",62,{"impact":17,"substance":18,"depth":100,"authority":178,"freshness":17,"relevant":22,"comment":179},13,"基于高分辨率遥感的区域土地利用变化实证研究，方法规范、数据翔实，对农业遥感监测有参考价值，但属地方性案例，公共影响有限。",[181],{"name":173,"url":174},[27,106,29,183,184],"印度农业","植被覆盖",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[187],{"code":132,"slug":188,"name":189,"path":190,"parent_code":37},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":40,"locked":14,"version":41,"method":42,"evidence":192},{"candidates":193,"agricultural_evidence":46},[194,200,205,212,216],{"code":33,"score":195,"supported":46,"title":196,"abstract":197,"tags":199},12,[],[49,50,198],"multispectral",[27,184,106],{"code":132,"score":101,"supported":46,"title":201,"abstract":202,"tags":204},[],[135,136,203],"geographic information",[],{"code":206,"score":20,"supported":46,"title":207,"abstract":208,"tags":211},"H2",[],[209,210],"土壤","soil",[],{"code":53,"score":22,"supported":14,"title":213,"abstract":214,"tags":215},[],[],[29],{"code":217,"score":22,"supported":14,"title":218,"abstract":219,"tags":220},"J3",[],[],[183],[222,223],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187",5,"10.31018\u002Fjans.v18i3.7774",{"doi":226,"openalex_id":228,"authors":229,"venue":173,"cited_by_count":79,"oa_url":174,"card":235,"direction":84,"ingested_from":86},"W7213942371",[230,232],{"name":231,"orcid":9},"J. C. Das",{"name":233,"orcid":234},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":236,"method":237,"finding":238,"direction":84,"opportunity":239},"利用高分辨率卫星影像分析印度阿萨姆邦纳冈地区2015-2024年土地利用\u002F覆盖变化。","LISS-IV 5.8米多光谱影像，最大似然分类，精度与Kappa系数评估。","农业用地和自然植被分别减少9.83%和6.14%，而林外树木和建设用地分别增加14.46%和0.82","可结合时序高分辨率影像与农户调查，探究林外树木扩张对农业韧性和碳汇的驱动机制。","2026-09-22T23:30:25.539323Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":254,"tags":256,"primary_topic":261,"secondary_topics":267,"classification":274,"search_phrases":308,"slug":311,"view_count":79,"doi":312,"paper":313,"created_at":339},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":19,"substance":251,"depth":18,"authority":252,"freshness":13,"relevant":22,"comment":253},22,14,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[255],{"name":247,"url":244},[257,258,27,259,260],"智慧农业","农业人工智能","氮素监测","紫花苜蓿",{"code":262,"slug":263,"name":264,"path":265,"parent_code":266},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[268,269],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":270,"slug":271,"name":272,"path":273,"parent_code":118},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed",{"status":40,"locked":14,"version":41,"method":42,"evidence":275},{"candidates":276,"agricultural_evidence":46},[277,282,288,294,303],{"code":33,"score":18,"supported":46,"title":278,"abstract":280,"tags":281},[279],"hyperspectral",[49,50,279],[27],{"code":262,"score":100,"supported":46,"title":283,"abstract":285,"tags":287},[284],"alfalfa",[286,284],"苜蓿",[260],{"code":270,"score":13,"supported":46,"title":289,"abstract":290,"tags":293},[],[291,286,292],"饲草","forage",[260],{"code":58,"score":13,"supported":46,"title":295,"abstract":296,"tags":302},[],[297,298,299,300,301],"深度学习","机器学习","machine learning","deep learning","decision support",[258],{"code":304,"score":22,"supported":14,"title":305,"abstract":306,"tags":307},"I1",[],[],[257],[309,310],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":312,"openalex_id":314,"authors":315,"venue":247,"cited_by_count":79,"oa_url":244,"card":334,"direction":84,"ingested_from":86},"W7223154576",[316,318,320,322,324,327,329,332],{"name":317,"orcid":9},"Shuai Fu",{"name":319,"orcid":9},"Yunhao Li",{"name":321,"orcid":9},"Chunli Miao",{"name":323,"orcid":9},"Jie Liu",{"name":325,"orcid":326},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":328,"orcid":9},"Yongjia Hu",{"name":330,"orcid":331},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":333,"orcid":9},"吴彩霞",{"tldr":335,"method":336,"finding":337,"direction":84,"opportunity":338},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":9,"content":9,"source_name":345,"source_url":9,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":349,"tags":351,"primary_topic":355,"secondary_topics":360,"classification":368,"search_phrases":389,"slug":392,"view_count":22,"doi":9,"paper":393,"created_at":401},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":18,"substance":251,"depth":18,"authority":252,"freshness":13,"relevant":22,"comment":348},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[350],{"name":345,"url":343},[257,27,352,353,354],"作物表型","无人机多光谱","小麦渍害",{"code":356,"slug":357,"name":358,"path":359,"parent_code":266},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[361,362],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":363,"slug":364,"name":365,"path":366,"parent_code":367},"H7","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":46},[371,376,380,385],{"code":356,"score":372,"supported":46,"title":373,"abstract":374,"tags":375},11,[358],[358],[],{"code":33,"score":372,"supported":46,"title":377,"abstract":378,"tags":379},[353],[],[352,27,353],{"code":363,"score":17,"supported":46,"title":381,"abstract":383,"tags":384},[382],"渍害",[],[],{"code":304,"score":22,"supported":14,"title":386,"abstract":387,"tags":388},[],[],[257],[390,391],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":9,"openalex_id":9,"authors":394,"venue":9,"cited_by_count":79,"oa_url":9,"card":395,"direction":84,"ingested_from":400},[],{"tldr":396,"method":397,"finding":398,"direction":84,"opportunity":399},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":419,"secondary_topics":420,"classification":421,"search_phrases":443,"slug":446,"view_count":79,"doi":447,"paper":448,"created_at":462},5391,"Spatio-temporal analysis and forecasting of land use\u002Fland cover change in East Medinipur district, India using CA-Markov model","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fgac.2026.23669","Land Use\u002FLand Cover (LU\u002FLC) serve as essential indicators of global environmental changes. This study examined the LU\u002FLC change in the East Medinipur district of West Bengal, India, using remote sensing data and the CA-Markov modeling approach. Through GIS tools and satellite imagery, the study maps, monitors, and analyzes LU\u002FLC changes over a 20-year period (2001–2021), utilizing data from Landsat 4–5 and Landsat 8. Analytical tools including ArcGIS 9, IDRISI Terset, and ERDAS 2014 are employed to project LU\u002FLC trends up to 2041. The results highlight significant transformations, notable reductions in agriculture, vegetation, barren land, and water bodies, alongside substantial increases in aquaculture and built-up areas from 2001 to 2021. Projections indicates that these trends will continue through 2041, with further declines in agriculture, vegetation, barren land, and water bodies, accompanied by continued growth in aquaculture and built-up areas.","土地利用\u002F土地覆盖（Land Use\u002FLand Cover，LU\u002FLC）是全球环境变化的重要指标。本研究利用遥感数据和CA-Markov建模方法，考察了印度西孟加拉邦东梅迪尼普尔县的土地利用\u002F土地覆盖变化。通过GIS工具和卫星影像，研究利用Landsat 4–5和Landsat 8数据，对2001—2021年20年间的土地利用\u002F土地覆盖变化进行了制图、监测与分析。研究采用ArcGIS 9、IDRISI Terset和ERDAS 2014等分析工具，对至2041年的土地利用\u002F土地覆盖趋势进行预测。结果表明，2001年至2021年间发生了显著转变，农业、植被、裸地和水体明显减少，水产养殖和建成区则大幅增加。预测显示，这些趋势将持续至2041年，农业、植被、裸地和水体进一步减少，水产养殖和建成区持续增长。","Geodesy and Cartography","2026-10-19T00:00:00Z",61,{"impact":17,"substance":18,"depth":100,"authority":195,"freshness":17,"relevant":22,"comment":412},"该研究基于遥感与CA-Markov模型分析印度区域土地利用变化，方法规范、结论明确，但属区域性案例，对国内三农与农业信息化的直接参考价值有限，可作为遥感应用类论文收录，不宜进入每日精选。",[414],{"name":408,"url":405},[416,49,29,417,418],"水产养殖","耕地变化","CA-Markov",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":422},{"candidates":423,"agricultural_evidence":46},[424,431,435,439],{"code":139,"score":425,"supported":46,"title":426,"abstract":427,"tags":430},7,[],[428,429],"水产","aquaculture",[416],{"code":33,"score":425,"supported":46,"title":432,"abstract":433,"tags":434},[],[49,50],[49],{"code":132,"score":140,"supported":46,"title":436,"abstract":437,"tags":438},[],[136],[],{"code":53,"score":22,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[29],[444,445],"Landsat 土地利用 遥感监测","土地利用 水产养殖 耕地变化 遥感","Landsat土地利用遥感监测-5391","10.3846\u002Fgac.2026.23669",{"doi":447,"openalex_id":449,"authors":450,"venue":408,"cited_by_count":79,"oa_url":456,"card":457,"direction":84,"ingested_from":86},"W7222963948",[451,454],{"name":452,"orcid":453},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":455,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":458,"method":459,"finding":460,"direction":84,"opportunity":461},"用遥感与CA-Markov模型分析印度东梅迪尼普尔2001-2021年土地利用变化并预测至2041年","Landsat影像、GIS与IDRISI\u002FERDAS，CA-Markov模型做L","农业、植被、裸地和水体持续减少，水产养殖与建设用地显著扩张并将延续至2041年。","可将CA-Markov与作物适宜性\u002F粮食安全模型耦合，评估养殖与建设用地扩张对农业生产的长期威胁。","2026-10-20T23:30:19.057951Z",{"id":464,"title":465,"url":466,"summary":467,"summary_zh":468,"content":9,"source_name":469,"source_url":466,"published_at":409,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":79,"score_detail":470,"sources":472,"tags":474,"primary_topic":477,"secondary_topics":478,"classification":479,"search_phrases":500,"slug":503,"view_count":79,"doi":504,"paper":505,"created_at":533},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",{"impact":79,"substance":79,"depth":79,"authority":79,"freshness":79,"relevant":79,"comment":471},"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。",[473],{"name":469,"url":466},[106,29,475,476],"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":480},{"candidates":481,"agricultural_evidence":46},[482,486,490,496],{"code":33,"score":425,"supported":46,"title":483,"abstract":484,"tags":485},[],[49,50],[106],{"code":132,"score":140,"supported":46,"title":487,"abstract":488,"tags":489},[],[136],[],{"code":491,"score":140,"supported":46,"title":492,"abstract":493,"tags":495},"I7",[],[494],"methodological framework",[],{"code":53,"score":22,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[29],[501,502],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":504,"openalex_id":506,"authors":507,"venue":469,"cited_by_count":79,"oa_url":466,"card":527,"direction":84,"ingested_from":86},"W7222945959",[508,511,514,516,519,522,525],{"name":509,"orcid":510},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":512,"orcid":513},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":515,"orcid":9},"Милан Радовић",{"name":517,"orcid":518},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":520,"orcid":521},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":523,"orcid":524},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":526,"orcid":9},"Manojlović Stefan",{"tldr":528,"method":529,"finding":530,"direction":531,"opportunity":532},"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"]