[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1252":3,"related-1252":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":28,"secondary_topics":34,"classification":35,"search_phrases":58,"slug":61,"view_count":19,"doi":62,"paper":63,"created_at":79},1252,"Digital visibility of regional agricultural brands in sustainable food systems: exploratory evidence from Zhejiang Province, China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1933197","Digital visibility is an important channel through which agricultural products and brands enter consumer information search. This exploratory study examines the online search visibility of agricultural brand and product keywords in Zhejiang Province, China. A candidate pool of 126 official brand\u002Fproduct entries was constructed, but only 21 keywords (16.7%) had retrievable and sufficiently interpretable Baidu Index data. The resulting non-random, availability-based analytical sample comprises 14 specific or brand-related keywords and seven product-category or place-based proxy keywords; it should not be treated as statistically representative of all agricultural brands in Zhejiang. Baidu Index data and Amap geocoding results were used for descriptive statistics, descriptive spatial analysis, keyword-group comparison, and a sensitivity analysis restricted to the 14 specific or brand-related keywords. Digital visibility was highly uneven, and the seven proxy keywords accounted for 84.5% of total search visibility. The sensitivity analysis confirmed uneven visibility among the more specific or brand-related keywords but substantially changed the city ranking, showing that the apparent concentration in several cities was largely proxy-driven. Because the 21 keyword observations were nested within only nine cities and multiple observations shared identical city-level characteristics, no inferential model of regional socioeconomic determinants was estimated. The findings therefore concern retrievable digital visibility, not actual sales or market recognition, and highlight the need to link broad product or place awareness to specific, searchable brand identities.",null,"Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z","论文",10,false,60,{"impact":16,"substance":17,"depth":18,"authority":16,"freshness":19,"relevant":20,"comment":21},12,18,16,2,1,"研究区域农业品牌数字可见性，数据详实但时效性低，对品牌数字化有参考价值。",[23],{"name":9,"url":6},[25,26,27],"数字农业","品牌建设","区域农业",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":36,"locked":13,"version":37,"method":38,"evidence":39},"classified","2026-10-22.2","rules",{"candidates":40,"agricultural_evidence":43},[41,48,53],{"code":29,"score":42,"supported":43,"title":44,"abstract":45,"tags":47},3,true,[],[46],"spatial analysis",[],{"code":49,"score":20,"supported":13,"title":50,"abstract":51,"tags":52},"E5",[],[],[26],{"code":54,"score":20,"supported":13,"title":55,"abstract":56,"tags":57},"I1",[],[],[25],[59,60],"区域农业 品牌建设 数字农业","区域农业 品牌建设","区域农业品牌建设数字农业-1252","10.3389\u002Ffsufs.2026.1933197",{"doi":62,"openalex_id":64,"authors":65,"venue":9,"cited_by_count":70,"oa_url":71,"card":72,"direction":76,"ingested_from":78},"W7204210992",[66,68],{"name":67,"orcid":8},"Weixin Zhu",{"name":69,"orcid":8},"Y. Fei",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1933197\u002Fpdf",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"探索浙江省农业品牌数字可见性，发现其高度不均且受产品类别影响。","百度指数与高德地理编码，描述统计与空间分析。","数字可见性不均，代理关键词占84.5%，城市排名受代理驱动。","数字乡村与农业信息化","可研究品牌数字可见性与实际销售关联，或开发提升特定品牌搜索可见性的策略。","openalex","2026-09-01T04:03:09.834440Z",{"total":81,"page":20,"page_size":81,"items":82},6,[83,173,265,336,397,462],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":92,"sources":98,"tags":100,"primary_topic":105,"secondary_topics":106,"classification":107,"search_phrases":148,"slug":151,"view_count":134,"doi":152,"paper":153,"created_at":172},3902,"ҚАЗАҚСТАННЫҢ ОҢТҮСТІК-ШЫҒЫСЫНДАҒЫ ТӘЛІМДІ ЖЕРЛЕРДІҢ ЭРОЗИЯЛЫҚ ҚАУІПТІЛІГІН ҚАШЫҚТАН ЗОНДТАУ ДЕРЕКТЕРІ МЕН RUSLE МОДЕЛІ НЕГІЗІНДЕ БАҒАЛАУ","https:\u002F\u002Fdoi.org\u002F10.52578\u002F3134-8556-2026-3-14-23","This study assesses potential soil erosion on arable land in the foothill zone of South-East Kazakhstan using the Revised Universal Soil Loss Equation (RUSLE), remote sensing data, and Geographic Information Systems (GIS). The research focuses on a 17.6-hectare agricultural plot located in the Almaty region. Sentinel-2 satellite imagery and the FABDEM_V1 global digital elevation model were used as the primary data sources for spatial analysis. The methodology involved the calculation and integration of the main RUSLE factors, including rainfall erosivity (R), soil erodibility (K), topographic factor (LS), cover-management factor (C), and support practice factor (P). The LS factor was derived from the FABDEM digital elevation model, while the vegetation cover factor (C) was estimated using the Normalized Difference Vegetation Index (NDVI) for two key agricultural periods: April (pre-growing season) and July (post-harvest period). Based on soil mechanical composition and humus content, the K factor was determined as 0.26. The results show that the average annual soil loss amounted to 10.01 t\u002Fha\u002Fyear in April and increased to 13.67 t\u002Fha\u002Fyear in July. The analysis revealed that erosion risk rises by approximately 36% after harvesting due to reduced vegetation cover. The maximum soil loss reached 55.46 t\u002Fha\u002Fyear in the most erosion-prone areas. The findings highlight that the post-harvest period represents the most critical phase for soil degradation in the study area. The study emphasizes the importance of implementing soil conservation measures and demonstrates the effectiveness of integrating RUSLE modeling with remote sensing and GIS technologies for sustainable land management.","本研究利用修正通用土壤流失方程（RUSLE）、遥感数据和地理信息系统（GIS），评估了哈萨克斯坦东南部山麓地带耕地的潜在土壤侵蚀。研究聚焦于阿拉木图地区一块17.6公顷的农业地块。采用Sentinel-2卫星影像和FABDEM_V1全球数字高程模型作为空间分析的主要数据来源。研究方法包括计算并整合RUSLE的主要因子，包括降雨侵蚀力（R）、土壤可蚀性（K）、地形因子（LS）、植被覆盖与管理因子（C）以及水土保持措施因子（P）。LS因子由FABDEM数字高程模型提取，植被覆盖因子（C）则利用归一化植被指数（NDVI）在两个关键农业时期进行估算：4月（生长季前）和7月（收获后）。根据土壤机械组成和腐殖质含量，确定K因子为0.26。结果表明，4月年均土壤流失量为10.01吨\u002F公顷\u002F年，7月增至13.67吨\u002F公顷\u002F年。分析显示，由于植被覆盖减少，收获后侵蚀风险上升约36%。在侵蚀最严重的区域，最大土壤流失量达到55.46吨\u002F公顷\u002F年。研究结果突出表明，收获后时期是研究区土壤退化最为关键的阶段。本研究强调了实施土壤保持措施的重要性，并证明了将RUSLE模型与遥感和GIS技术相结合用于可持续土地管理的有效性。","Agricultural Systems and Natural Resources","2026-09-30T00:00:00Z",72,{"impact":16,"substance":93,"depth":94,"authority":95,"freshness":96,"relevant":20,"comment":97},21,17,13,9,"基于RUSLE模型与遥感数据评估哈萨克斯坦东南部耕地侵蚀风险，方法规范、数据具体，对干旱区数字农业与水土保持有参考价值，但属区域案例研究，公共影响有限。",[99],{"name":89,"url":86},[25,101,102,103,104],"遥感","土壤侵蚀","水土保持","RUSLE",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":108},{"candidates":109,"agricultural_evidence":43},[110,118,125,132,138,144],{"code":29,"score":96,"supported":43,"title":111,"abstract":112,"tags":117},[],[113,114,115,46,116],"地理信息","空间分析","GIS","geographic information",[],{"code":119,"score":120,"supported":43,"title":121,"abstract":122,"tags":124},"I3",7,[],[101,123],"remote sensing",[101],{"code":126,"score":81,"supported":43,"title":127,"abstract":128,"tags":131},"H2",[],[129,130],"土壤","soil",[],{"code":133,"score":134,"supported":43,"title":135,"abstract":136,"tags":137},"H6",4,[],[103],[103],{"code":139,"score":42,"supported":43,"title":140,"abstract":141,"tags":143},"I7",[],[142],"研究方法",[],{"code":54,"score":20,"supported":13,"title":145,"abstract":146,"tags":147},[],[],[25],[149,150],"哈萨克斯坦 阿拉木图 RUSLE 土壤侵蚀","Sentinel-2 NDVI 土壤流失","哈萨克斯坦阿拉木图RUSLE土壤侵蚀-3902","10.52578\u002F3134-8556-2026-3-14-23",{"doi":152,"openalex_id":154,"authors":155,"venue":89,"cited_by_count":70,"oa_url":86,"card":166,"direction":170,"ingested_from":78},"W7214985337",[156,158,160,162,164],{"name":157,"orcid":8},"Д. Кайнушева",{"name":159,"orcid":8},"Р. Жапаев",{"name":161,"orcid":8},"Е. Кенжехан",{"name":163,"orcid":8},"Д. Кыдырбаева",{"name":165,"orcid":8},"Ә. Мақұлбек",{"tldr":167,"method":168,"finding":169,"direction":170,"opportunity":171},"用RUSLE模型和遥感数据评估哈萨克斯坦东南部耕地土壤侵蚀风险。","RUSLE模型、Sentinel-2影像、FABDEM高程数据与GIS空间分析。","年均土壤流失量4月为10.01 t\u002Fha，7月增至13.67 t\u002Fha，收获后侵蚀风险上升约36%。","农业遥感与作物表型","可结合多时相遥感与机器学习，动态预测收获后侵蚀热点，优化保护性耕作措施。","2026-10-01T23:30:29.218572Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":8,"source_name":179,"source_url":176,"published_at":90,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":180,"score_detail":181,"sources":185,"tags":187,"primary_topic":190,"secondary_topics":196,"classification":202,"search_phrases":248,"slug":251,"view_count":20,"doi":252,"paper":253,"created_at":264},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。","OpenAlex",78,{"impact":17,"substance":182,"depth":94,"authority":95,"freshness":183,"relevant":20,"comment":184},22,8,"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[186],{"name":179,"url":176},[25,188,101,115,189],"粮食安全","灌溉适宜性",{"code":191,"slug":192,"name":193,"path":194,"parent_code":195},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[197,201],{"code":119,"slug":198,"name":199,"path":200,"parent_code":33},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":36,"locked":13,"version":37,"method":38,"evidence":203},{"candidates":204,"agricultural_evidence":43},[205,211,216,220,226,232,238,244],{"code":191,"score":94,"supported":43,"title":206,"abstract":207,"tags":210},[192],[208,192,209],"灌溉","evapotranspiration",[],{"code":119,"score":212,"supported":43,"title":213,"abstract":214,"tags":215},15,[123],[101,123],[101],{"code":29,"score":96,"supported":43,"title":217,"abstract":218,"tags":219},[115],[],[115],{"code":221,"score":120,"supported":43,"title":222,"abstract":223,"tags":225},"A8",[],[188,224],"food security",[188],{"code":227,"score":42,"supported":43,"title":228,"abstract":229,"tags":231},"H7",[],[230],"干旱",[],{"code":233,"score":42,"supported":43,"title":234,"abstract":235,"tags":237},"H8",[],[236],"可持续农业",[],{"code":239,"score":42,"supported":43,"title":240,"abstract":241,"tags":243},"J1",[],[242],"农业规划",[],{"code":54,"score":20,"supported":13,"title":245,"abstract":246,"tags":247},[],[],[25],[249,250],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":252,"openalex_id":254,"authors":255,"venue":8,"cited_by_count":70,"oa_url":8,"card":259,"direction":170,"ingested_from":78},"W7214955379",[256],{"name":257,"orcid":258},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":260,"method":261,"finding":262,"direction":170,"opportunity":263},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":8,"source_name":271,"source_url":268,"published_at":272,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":273,"score_detail":274,"sources":277,"tags":279,"primary_topic":284,"secondary_topics":285,"classification":286,"search_phrases":315,"slug":318,"view_count":134,"doi":319,"paper":320,"created_at":335},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。","Preprints.org","2026-09-22T00:00:00Z",67,{"impact":18,"substance":275,"depth":94,"authority":81,"freshness":183,"relevant":20,"comment":276},20,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[278],{"name":271,"url":268},[25,280,281,282,283],"智慧农业","农业人工智能","巴西农业","农村数字化",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":287},{"candidates":288,"agricultural_evidence":43},[289,294,298,304,310],{"code":54,"score":183,"supported":43,"title":290,"abstract":291,"tags":293},[],[25,292],"digital agriculture",[25,280],{"code":29,"score":81,"supported":43,"title":295,"abstract":296,"tags":297},[],[114,46],[],{"code":299,"score":42,"supported":43,"title":300,"abstract":301,"tags":303},"G1",[],[302],"技术培训",[],{"code":305,"score":42,"supported":43,"title":306,"abstract":307,"tags":309},"I6",[],[308],"agricultural data",[],{"code":311,"score":20,"supported":13,"title":312,"abstract":313,"tags":314},"I2",[],[],[281],[316,317],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367","10.20944\u002Fpreprints202609.1880.v1",{"doi":319,"openalex_id":321,"authors":322,"venue":271,"cited_by_count":70,"oa_url":268,"card":330,"direction":76,"ingested_from":78},"W7214109425",[323,326,328],{"name":324,"orcid":325},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":327,"orcid":8},"Eduardo Dias",{"name":329,"orcid":8},"Lidia Scoton",{"tldr":331,"method":332,"finding":333,"direction":76,"opportunity":334},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","2026-09-24T23:30:27.046035Z",{"id":337,"title":338,"url":339,"summary":340,"summary_zh":341,"content":8,"source_name":342,"source_url":339,"published_at":343,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":344,"score_detail":345,"sources":348,"tags":350,"primary_topic":352,"secondary_topics":353,"classification":354,"search_phrases":377,"slug":380,"view_count":70,"doi":381,"paper":382,"created_at":396},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":183,"substance":17,"depth":346,"authority":16,"freshness":96,"relevant":20,"comment":347},14,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[349],{"name":342,"url":339},[101,351,102,115,103],"土地利用",{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},[],{"status":36,"locked":13,"version":37,"method":38,"evidence":355},{"candidates":356,"agricultural_evidence":43},[357,361,365,369,373],{"code":29,"score":12,"supported":43,"title":358,"abstract":359,"tags":360},[],[113,115,116],[115],{"code":119,"score":120,"supported":43,"title":362,"abstract":363,"tags":364},[],[101,123],[101],{"code":126,"score":81,"supported":43,"title":366,"abstract":367,"tags":368},[],[129,130],[],{"code":191,"score":42,"supported":43,"title":370,"abstract":371,"tags":372},[],[208],[],{"code":133,"score":19,"supported":13,"title":374,"abstract":375,"tags":376},[],[],[351,103],[378,379],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":381,"openalex_id":383,"authors":384,"venue":342,"cited_by_count":70,"oa_url":8,"card":390,"direction":170,"ingested_from":78},"W7223117733",[385,387],{"name":386,"orcid":8},"MERİÇ KURT",{"name":388,"orcid":389},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":391,"method":392,"finding":393,"direction":394,"opportunity":395},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":398,"title":399,"url":400,"summary":401,"summary_zh":402,"content":8,"source_name":403,"source_url":400,"published_at":404,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":70,"score_detail":405,"sources":407,"tags":409,"primary_topic":413,"secondary_topics":417,"classification":419,"search_phrases":439,"slug":442,"view_count":70,"doi":443,"paper":444,"created_at":461},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":70,"substance":70,"depth":70,"authority":70,"freshness":70,"relevant":70,"comment":406},"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。",[408],{"name":403,"url":400},[410,411,351,412],"机器学习","遥感监测","城市扩张",{"code":311,"slug":414,"name":415,"path":416,"parent_code":33},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[418],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"status":36,"locked":13,"version":37,"method":38,"evidence":420},{"candidates":421,"agricultural_evidence":43},[422,427,431,435],{"code":311,"score":212,"supported":43,"title":423,"abstract":425,"tags":426},[424],"machine learning",[410,424],[410],{"code":29,"score":96,"supported":43,"title":428,"abstract":429,"tags":430},[],[114,115,46],[],{"code":119,"score":120,"supported":43,"title":432,"abstract":433,"tags":434},[],[101,123],[411],{"code":133,"score":20,"supported":13,"title":436,"abstract":437,"tags":438},[],[],[351],[440,441],"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":443,"openalex_id":445,"authors":446,"venue":403,"cited_by_count":70,"oa_url":400,"card":456,"direction":170,"ingested_from":78},"W7223022155",[447,450,452,454],{"name":448,"orcid":449},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":451,"orcid":8},"Goanar Jal Dup",{"name":453,"orcid":8},"Weyesa Merga Chemeda",{"name":455,"orcid":8},"Shankar Karuppannan",{"tldr":457,"method":458,"finding":459,"direction":170,"opportunity":460},"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":463,"title":464,"url":465,"summary":466,"summary_zh":467,"content":8,"source_name":468,"source_url":465,"published_at":469,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":70,"score_detail":470,"sources":472,"tags":474,"primary_topic":478,"secondary_topics":479,"classification":485,"search_phrases":501,"slug":504,"view_count":70,"doi":505,"paper":506,"created_at":518},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","2026-10-17T00:00:00Z",{"impact":70,"substance":70,"depth":70,"authority":70,"freshness":70,"relevant":70,"comment":471},"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。",[473],{"name":468,"url":465},[475,411,476,477],"防灾减灾","洪水风险评估","GIS应用",{"code":119,"slug":198,"name":199,"path":200,"parent_code":33},[480,481],{"code":29,"slug":30,"name":31,"path":32,"parent_code":33},{"code":227,"slug":482,"name":483,"path":484,"parent_code":195},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":36,"locked":13,"version":37,"method":38,"evidence":486},{"candidates":487,"agricultural_evidence":43},[488,492,496],{"code":119,"score":212,"supported":43,"title":489,"abstract":490,"tags":491},[123],[101,123],[411],{"code":29,"score":346,"supported":43,"title":493,"abstract":494,"tags":495},[115],[113,115],[],{"code":227,"score":183,"supported":43,"title":497,"abstract":498,"tags":500},[],[230,499],"drought",[476,475],[502,503],"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":505,"openalex_id":507,"authors":508,"venue":468,"cited_by_count":70,"oa_url":512,"card":513,"direction":170,"ingested_from":78},"W7222885988",[509],{"name":510,"orcid":511},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":514,"method":515,"finding":516,"direction":170,"opportunity":517},"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"]