[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1615":3,"related-1615":66},{"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":22,"secondary_topics":28,"classification":29,"search_phrases":48,"slug":49,"view_count":15,"doi":50,"paper":51,"created_at":65},1615,"New inequalities in the urban agri-food system—insights from Bengaluru, South India","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1860147","Food insecurity and urbanization are both on the rise in the Global South. Cities are considered to be beneficial for both farmers and consumers by providing market for farm produce and affordable food, respectively. Food urbanism—a phenomenon manifested within and beyond urban geographies- involves conspicuous consumption and revealed preference for safe, high value raw foods. This phenomenon triggers the emergence of alternative foods networks (AFN), marking a new phase in the evolution of urban agri-food systems (AFS). Paper explores the question how best to characterize and trace the changes in urban AFS along with their outcomes, in the context of emerging supply channels for fresh food in the city of Bengaluru. It examines the inequality outcomes of the distribution of benefits from food urbanism and related AFNs, among consumers and producers. Paper unpacks the coupled phenomena of agricultural production and urban food consumption, and employs a historical perspective to contextualize, and trace the evolution of urban AFS in the Global South. In the process, it characterizes nascent changes in urban AFS and foregrounds their distributional, sustainability and welfare outcomes among consumers and producers. Conceptual analysis applies the framework of metabolic rifts to understand the dynamic interface of food consumption and production and exposes multiple alienations in the current state of the urban food system as also in the alternative regimes in AFNs. Analysis of the inequalities embedded in food urbanism and AFNs uses a composite lens where the frameworks of regional, circular and moral economy converge. Based on the empirical study in and around the city of Bengaluru, using store exit polls and surveys among consumers, interviews as well as FGDs with consumers and producers, interactive consumer-producer forums and laboratory analysis of soil and plant samples, the paper argues that the emerging AFNs are not necessarily sustainable or equitable and are distributionally skewed against the poor among urban consumers and farmers. It identifies steps to reconfigure AFNs for progressive and sustainable transformation of urban AFS.","在全球南方，粮食不安全和城市化问题均在加剧。城市被认为对农民和消费者都有利，分别为农产品提供市场，为消费者提供可负担的食物。食物都市主义——一种在城市内外地理空间显现的现象——涉及对安全、高价值生鲜食品的炫耀性消费和显性偏好。这一现象催生了替代食物网络（AFN）的出现，标志着城市农业食物系统（AFS）演变进入新阶段。本文探讨了在班加罗尔市生鲜食品新兴供应渠道背景下，如何最佳地表征和追踪城市AFS的变化及其结果。研究考察了食物都市主义及相关AFN在消费者和生产者之间利益分配的不平等结果。本文解析了农业生产与城市食物消费的耦合现象，并采用历史视角对全球南方城市AFS进行情境化梳理和演变追踪。在此过程中，本文表征了城市AFS的初期变化，并凸显了其在消费者和生产者之间的分配性、可持续性和福利结果。概念分析运用代谢断裂框架来理解食物消费与生产的动态界面，揭示了当前城市食物系统以及AFN替代模式中的多重异化。对食物都市主义和AFN中嵌入的不平等分析采用复合视角，融合了区域经济、循环经济和道德经济的框架。基于班加罗尔市及其周边的实证研究，运用店铺出口民意调查和消费者调查、对消费者和生产者的访谈及焦点小组讨论（FGD）、消费者—生产者互动论坛以及土壤和植物样本的实验室分析，本文论证了新出现的AFN未必具有可持续性或公平性，且在分配上偏向于不利于城市消费者和农民中的贫困群体。本文确定了重新配置AFN的步骤，以推动城市AFS的进步性和可持续性转型。",null,"Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为城市农业食品系统不平等，与三农信息化关联弱，不入选。",[19],{"name":10,"url":6},[21],"农业信息化",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"classified","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":37},[35,42],{"code":23,"score":36,"supported":37,"title":38,"abstract":39,"tags":41},6,true,[],[40,24],"土壤",[],{"code":43,"score":44,"supported":14,"title":45,"abstract":46,"tags":47},"I1",1,[],[],[21],[],"Newinequalitiesintheurbanagri-foodsystem-1615","10.3389\u002Ffsufs.2026.1860147",{"doi":50,"openalex_id":52,"authors":53,"venue":10,"cited_by_count":15,"oa_url":57,"card":58,"direction":62,"ingested_from":64},"W7207840366",[54],{"name":55,"orcid":56},"Seema Purushothaman","https:\u002F\u002Forcid.org\u002F0000-0002-8161-8763","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1860147\u002Fpdf",{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"研究印度班加罗尔城市农业食品系统中新兴替代食品网络的不平等结果。","结合历史视角、代谢断裂框架及区域\u002F循环\u002F道德经济复合透镜，采用消费者调查、访谈、","替代食品网络并非必然可持续或公平，其利益分配偏向富裕消费者和农民，对穷人不公。","数字乡村与农业信息化","可探索数字技术如何改善城市农业食品系统中替代食品网络的公平性，特别是对弱势群体的包容性。","openalex","2026-09-04T23:30:08.372943Z",{"total":36,"page":44,"page_size":36,"items":67},[68,151,222,279,345,427],{"id":69,"title":70,"url":71,"summary":72,"summary_zh":9,"content":9,"source_name":73,"source_url":71,"published_at":74,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":75,"score_detail":76,"sources":83,"tags":85,"primary_topic":90,"secondary_topics":96,"classification":104,"search_phrases":127,"slug":130,"view_count":123,"doi":131,"paper":132,"created_at":150},3500,"Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10333-026-01102-6","Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam。Paddy and Water Environment","Paddy and Water Environment","2026-09-24T00:00:00Z",67,{"impact":77,"substance":78,"depth":79,"authority":80,"freshness":81,"relevant":44,"comment":82},12,18,16,13,8,"核心期刊论文，遥感驱动土壤肥力建模与水稻适宜性制图，方法有参考价值但属区域案例，未达每日精选门槛。",[84],{"name":73,"url":71},[86,21,87,88,89],"智慧农业","遥感","土壤肥力","水稻种植",{"code":91,"slug":92,"name":93,"path":94,"parent_code":95},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[97,98],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"code":99,"slug":100,"name":101,"path":102,"parent_code":103},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":30,"locked":14,"version":31,"method":32,"evidence":105},{"candidates":106,"agricultural_evidence":37},[107,113,117,122],{"code":91,"score":108,"supported":37,"title":109,"abstract":110,"tags":112},15,[92],[92,111],"paddy",[89],{"code":23,"score":77,"supported":37,"title":114,"abstract":115,"tags":116},[24],[24],[88],{"code":99,"score":77,"supported":37,"title":118,"abstract":120,"tags":121},[119],"remote sensing",[119],[87],{"code":43,"score":123,"supported":14,"title":124,"abstract":125,"tags":126},2,[],[],[21,86],[128,129],"Assam 洪泛平原 水稻 遥感","土壤肥力 遥感 建模","Assam洪泛平原水稻遥感-3500","10.1007\u002Fs10333-026-01102-6",{"doi":131,"openalex_id":133,"authors":134,"venue":73,"cited_by_count":15,"oa_url":9,"card":9,"direction":149,"ingested_from":64},"W7214202229",[135,137,140,142,145,147],{"name":136,"orcid":9},"Pompy Deka",{"name":138,"orcid":139},"Jwngsar Moshahary","https:\u002F\u002Forcid.org\u002F0000-0002-3409-6691",{"name":141,"orcid":9},"Mrinal Choudhury",{"name":143,"orcid":144},"Perves Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2114-1043",{"name":146,"orcid":9},"Britan Rahman",{"name":148,"orcid":9},"Ranjit Sarma","农业遥感与作物表型","2026-09-25T23:30:30.923946Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"primary_topic":169,"secondary_topics":170,"classification":171,"search_phrases":198,"slug":201,"view_count":123,"doi":202,"paper":203,"created_at":221},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":77,"substance":161,"depth":162,"authority":80,"freshness":81,"relevant":44,"comment":163},20,17,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[165],{"name":157,"url":154},[86,167,21,87,168],"农业遥感","土地利用",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":172},{"candidates":173,"agricultural_evidence":37},[174,178,185,189,193],{"code":23,"score":36,"supported":37,"title":175,"abstract":176,"tags":177},[],[40,24],[],{"code":179,"score":180,"supported":37,"title":181,"abstract":182,"tags":184},"H3",3,[],[183],"水资源",[],{"code":43,"score":123,"supported":14,"title":186,"abstract":187,"tags":188},[],[],[21,86],{"code":99,"score":123,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[167,87],{"code":194,"score":44,"supported":14,"title":195,"abstract":196,"tags":197},"H6",[],[],[168],[199,200],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":202,"openalex_id":204,"authors":205,"venue":157,"cited_by_count":15,"oa_url":214,"card":215,"direction":220,"ingested_from":64},"W7213644047",[206,208,210,212],{"name":207,"orcid":9},"Sreerama Naik Naik S R",{"name":209,"orcid":9},"T K Prasad",{"name":211,"orcid":9},"Feba Jose Jasmine",{"name":213,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":216,"method":217,"finding":218,"direction":149,"opportunity":219},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":223,"title":224,"url":225,"summary":226,"summary_zh":9,"content":9,"source_name":227,"source_url":225,"published_at":228,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":229,"score_detail":230,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":240,"classification":241,"search_phrases":257,"slug":260,"view_count":44,"doi":261,"paper":262,"created_at":278},2745,"A novel framework for integrating diverse environmental effects of agricultural soil for sustainable remediation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104989","A novel framework for integrating diverse environmental effects of agricultural soil for sustainable remediation。Agricultural Systems","Agricultural Systems","2026-09-16T00:00:00Z",55,{"impact":81,"substance":77,"depth":231,"authority":80,"freshness":81,"relevant":44,"comment":232},14,"核心期刊论文，提出农业土壤多元环境效应整合框架，方法有新意但偏学术，产业与政策影响有限，可作为专业参考而非每日精选头条。",[234],{"name":227,"url":225},[21,236,237,238],"可持续农业","土壤修复","农业环境",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":242},{"candidates":243,"agricultural_evidence":37},[244,248,253],{"code":23,"score":77,"supported":37,"title":245,"abstract":246,"tags":247},[24],[24],[237],{"code":249,"score":44,"supported":14,"title":250,"abstract":251,"tags":252},"H8",[],[],[236],{"code":43,"score":44,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[21],[258,259],"农业信息化 可持续农业 农业环境 土壤修复","农业信息化 可持续农业","农业信息化可持续农业农业环境土壤修复-2745","10.1016\u002Fj.agsy.2026.104989",{"doi":261,"openalex_id":263,"authors":264,"venue":227,"cited_by_count":15,"oa_url":9,"card":9,"direction":9,"ingested_from":64},"W7213415381",[265,267,270,273,276],{"name":266,"orcid":9},"Yuanliang Jin",{"name":268,"orcid":269},"Muhan Qin","https:\u002F\u002Forcid.org\u002F0000-0002-7887-6833",{"name":271,"orcid":272},"Bin Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4268-6074",{"name":274,"orcid":275},"Zhenhuan Liu","https:\u002F\u002Forcid.org\u002F0000-0001-9932-9225",{"name":277,"orcid":9},"Shengqi Qi","2026-09-17T23:30:04.767424Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":291,"tags":293,"primary_topic":298,"secondary_topics":299,"classification":305,"search_phrases":324,"slug":327,"view_count":15,"doi":328,"paper":329,"created_at":344},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":81,"substance":161,"depth":162,"authority":289,"freshness":13,"relevant":44,"comment":290},9,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[292],{"name":285,"url":282},[86,294,295,296,297],"农业人工智能","主动学习","线虫检测","土壤采样",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[300],{"code":301,"slug":302,"name":303,"path":304,"parent_code":103},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":30,"locked":14,"version":31,"method":32,"evidence":306},{"candidates":307,"agricultural_evidence":37},[308,312,317],{"code":23,"score":231,"supported":37,"title":309,"abstract":310,"tags":311},[24],[40,24],[],{"code":301,"score":289,"supported":37,"title":313,"abstract":315,"tags":316},[314],"artificial intelligence",[],[294],{"code":43,"score":318,"supported":37,"title":319,"abstract":320,"tags":323},7,[],[321,322],"精准农业","precision agriculture",[86],[325,326],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":328,"openalex_id":330,"authors":331,"venue":285,"cited_by_count":15,"oa_url":282,"card":338,"direction":342,"ingested_from":64},"W7223082195",[332,334,336],{"name":333,"orcid":9},"Amine Ben Salah",{"name":335,"orcid":9},"Ines Trabelsi",{"name":337,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":339,"method":340,"finding":341,"direction":342,"opportunity":343},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z",{"id":346,"title":347,"url":348,"summary":349,"summary_zh":350,"content":9,"source_name":351,"source_url":348,"published_at":352,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":353,"score_detail":354,"sources":357,"tags":359,"primary_topic":365,"secondary_topics":366,"classification":367,"search_phrases":389,"slug":392,"view_count":15,"doi":393,"paper":394,"created_at":426},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":79,"substance":355,"depth":78,"authority":231,"freshness":289,"relevant":44,"comment":356},22,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[358],{"name":351,"url":348},[360,361,362,363,364],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":368},{"candidates":369,"agricultural_evidence":37},[370,374,378,385],{"code":23,"score":231,"supported":37,"title":371,"abstract":372,"tags":373},[24],[40,24],[],{"code":99,"score":318,"supported":37,"title":375,"abstract":376,"tags":377},[],[87,119],[360],{"code":379,"score":36,"supported":37,"title":380,"abstract":381,"tags":384},"H7",[],[382,383],"气候适应","干旱",[],{"code":194,"score":44,"supported":14,"title":386,"abstract":387,"tags":388},[],[],[363],[390,391],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":393,"openalex_id":395,"authors":396,"venue":351,"cited_by_count":15,"oa_url":348,"card":421,"direction":149,"ingested_from":64},"W7223069171",[397,399,401,403,406,408,411,414,416,418],{"name":398,"orcid":9},"Yue Li",{"name":400,"orcid":9},"Mengfei Shi",{"name":402,"orcid":9},"Fei Zhang",{"name":404,"orcid":405},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":407,"orcid":9},"Gowhar Meraj",{"name":409,"orcid":410},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":412,"orcid":413},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":415,"orcid":9},"Yuanfang Chai",{"name":417,"orcid":9},"Hongtao Xu",{"name":419,"orcid":420},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":422,"method":423,"finding":424,"direction":149,"opportunity":425},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":433,"source_url":430,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":434,"score_detail":435,"sources":438,"tags":440,"primary_topic":444,"secondary_topics":449,"classification":452,"search_phrases":486,"slug":489,"view_count":15,"doi":490,"paper":491,"created_at":508},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",77,{"impact":79,"substance":436,"depth":162,"authority":80,"freshness":13,"relevant":44,"comment":437},21,"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。",[439],{"name":433,"url":430},[86,441,360,442,443],"机器学习","土壤墒情","冬小麦",{"code":445,"slug":446,"name":447,"path":448,"parent_code":95},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[450,451],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"code":301,"slug":302,"name":303,"path":304,"parent_code":103},{"status":30,"locked":14,"version":31,"method":32,"evidence":453},{"candidates":454,"agricultural_evidence":37},[455,461,465,470,475,481],{"code":445,"score":456,"supported":37,"title":457,"abstract":459,"tags":460},26,[458,446],"winter wheat",[447,458,446],[443],{"code":23,"score":108,"supported":37,"title":462,"abstract":463,"tags":464},[24],[40,24],[442],{"code":301,"score":108,"supported":37,"title":466,"abstract":468,"tags":469},[467],"machine learning",[441,467],[441],{"code":99,"score":13,"supported":37,"title":471,"abstract":472,"tags":474},[],[87,119,473],"multispectral",[360],{"code":179,"score":36,"supported":37,"title":476,"abstract":477,"tags":480},[],[478,479],"灌溉","irrigation",[],{"code":43,"score":482,"supported":37,"title":483,"abstract":484,"tags":485},4,[],[86],[86],[487,488],"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":490,"openalex_id":492,"authors":493,"venue":433,"cited_by_count":15,"oa_url":430,"card":503,"direction":220,"ingested_from":64},"W7223156204",[494,497,499,501],{"name":495,"orcid":496},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":498,"orcid":9},"Yi Zhang",{"name":500,"orcid":9},"WEI Lei",{"name":502,"orcid":9},"Chengcai Zhang",{"tldr":504,"method":505,"finding":506,"direction":149,"opportunity":507},"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。","2026-10-22T11:20:18.887301Z"]