[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-968":3,"related-968":60},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":27,"secondary_topics":33,"classification":34,"search_phrases":54,"slug":57,"view_count":58,"doi":8,"paper":8,"created_at":59},968,"中国农科院资划所：揭示南方灌区土壤酸化多维驱动机制","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F","近日中国农业科学院农业资源与农业区划研究所联合多家协同单位，针对我国南方灌区土壤酸化加剧、制约水稻产能提升的产业痛点，系统揭示了氮沉降、作物吸收、酸雨输入、有机酸代谢「四维」驱动机制，提出分区分类的改良对策。该研究为南方红壤区酸化耕地治理与产能提升提供原创理论与靶向技术支撑。依托中国农科院集中突破机制，研究成果已在江西、湖南、广东三省红壤区开展田间试验示范，为保障国家口粮绝对安全和南方灌区绿色发展贡献农科方案。",null,"*   [![Image 1: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002F01470ea4e2424d42a168333cf811f005.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd9537ce4a367482e8f8ff43692d28822.htm) [仿生“外衣”延长水果保鲜期](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd9537ce4a367482e8f8ff43692d28822.htm)\n\n​近日，中国农业科学院农业基因组研究所开发出水果仿生保鲜“外衣”，能让樱桃、番茄等水果的保鲜期延长一周以上。相关研究成果...\n\n[2026.08.25](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd9537ce4a367482e8f8ff43692d28822.htm)  \n*   [![Image 2: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002F2821cb7635b4427ca55f6ff3afda455b.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F4df8e4fc55874bd190c9d48b0581afe1.htm) [人参功效成分遗传密码获解析](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F4df8e4fc55874bd190c9d48b0581afe1.htm)\n\n近日，中国农业科学院特产研究所联合基因组所开展了人参群体遗传学研究，揭示人工驯化影响三萜皂苷积累模式的遗传基础，发现了调...\n\n[2026.08.25](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F4df8e4fc55874bd190c9d48b0581afe1.htm)  \n*   [![Image 3: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002Fc8beea6d1e514d4696b05c3ff0a30d77.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd564f5f8a81f461c9642146b42cb6603.htm) [解码猪基因表达调控密码](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd564f5f8a81f461c9642146b42cb6603.htm)\n\n近日，中国农业科学院农业基因组研究所农业基因编辑技术创新团队开发了一套家畜全基因组等位基因特异性表达分析新方法，解码猪基...\n\n[2026.08.24](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002Fd564f5f8a81f461c9642146b42cb6603.htm)  \n*   [![Image 4: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002Fecc1c44ae4954dc99f0cc2c2b3d65095.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F94b1b04f259b4199bbf22855a7507f71.htm) [海南五指山大叶种红茶特征品质被挖掘](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F94b1b04f259b4199bbf22855a7507f71.htm)\n\n近日，中国农业科学院茶叶研究所茶叶精准加工创新团队在海南五指山大叶种红茶特征品质挖掘与形成机制解析方面取得新进展，系统揭...\n\n[2026.08.24](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F94b1b04f259b4199bbf22855a7507f71.htm)  \n*   [![Image 5: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002F377304b58b9a4472949e1a9bba8d83a6.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1d4032a5b59140ff95a42ccb12f93026.htm) [5种农药溶液国家二级标准物质研制成功](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1d4032a5b59140ff95a42ccb12f93026.htm)\n\n近日，中国农业科学院农业质量标准与检测技术研究所农业标准物质创新团队研制出5种农药溶液国家二级标准物质，包括甲醇中啶虫脒...\n\n[2026.08.24](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1d4032a5b59140ff95a42ccb12f93026.htm)  \n*   [![Image 6: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002Fbb4a44fd8e0442849519d840f6736733.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F5ebe701674334d5da62a332488dd5247.htm) [装上“挂钩”让鸡二联疫苗保护力倍增](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F5ebe701674334d5da62a332488dd5247.htm)\n\n最近，中国农业科学院上海兽医研究所通过给病毒抗原装上“挂钩”，使其能被疫苗高效携带，显著提升了疫苗抵御法氏囊病毒感染的能...\n\n[2026.08.24](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F5ebe701674334d5da62a332488dd5247.htm)  \n*   [![Image 7: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002F18cce9c6f19a4fdbba7cdfefdf1f1600.png)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F64e83ccdab5c432e90d4dc3461f47743.htm) [新型预测框架助力小麦育种降本增效](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F64e83ccdab5c432e90d4dc3461f47743.htm)\n\n近日，中国农业科学院农业环境与可持续发展研究所智慧气象与农业气候资源利用创新团队提出塑性感知基因组选择框架，实现了跨环境...\n\n[2026.08.13](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F64e83ccdab5c432e90d4dc3461f47743.htm)  \n*   [![Image 8: 3](https:\u002F\u002Fcaas.cn\u002Fstatic2022\u002Fimages\u002Fkyhd.jpg)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1583ba500276409a9de0b852e1214852.htm) [泛基因组研究揭示黄瓜\"走遍全球\"的基因秘密](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1583ba500276409a9de0b852e1214852.htm)\n\n近日，中国农业科学院蔬菜花卉研究所揭示了结构变异驱动黄瓜适应性进化与性状演化的遗传机制。相关研究成果发表在《自然·遗传学...\n\n[2026.08.12](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F1583ba500276409a9de0b852e1214852.htm)  \n*   [![Image 9: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002F5344567f4299424b93dd58c200cee195.jpg)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F2234b1e6560d4b92ba362f575c37dfdd.htm) [花生抗青枯病种质实现抗病优质聚合新突破](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F2234b1e6560d4b92ba362f575c37dfdd.htm)\n\n近日，抗病花生种质鉴定暨田间观摩会在中国农业科学院油料作物研究所召开，来自山东省花生研究所等国内农业科研单位的专家在现场...\n\n[2026.08.12](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F2234b1e6560d4b92ba362f575c37dfdd.htm)  \n*   [![Image 10: 1](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-08\u002Fab7bc244dd3041c69089f771d727e239.jpg)](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F3d7c8f211f2b4ea19a418d76047eec5f.htm) [西瓜种子大小调控的全新分子通路被解析](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F3d7c8f211f2b4ea19a418d76047eec5f.htm)\n\n近日，中国农业科学院郑州果树研究所西瓜甜瓜种质资源团队解析西瓜种子大小调控的全新分子通路，相关研究成果发表在《植物通讯（...\n\n[2026.08.11](https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fkyhd\u002F3d7c8f211f2b4ea19a418d76047eec5f.htm)","中国农业科学院 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2026-08-18\u002F19","2026-08-18T00:00:00Z",77,{"impact":121,"substance":122,"depth":123,"authority":76,"freshness":124,"relevant":62,"comment":125},22,18,15,9,"聚焦黑土保护与智慧施肥，院士报告含前沿成果，属农业科技领域重要学术活动，信息增量与权威性较好。",[127],{"name":117,"url":114},[21,129,130,131,132],"建院70周年","哈尔滨","土壤资源","农业科技管理",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[],{"status":35,"locked":14,"version":36,"method":37,"evidence":136},{"candidates":137,"agricultural_evidence":42},[138,142],{"code":28,"score":41,"supported":42,"title":139,"abstract":140,"tags":141},[44],[44],[],{"code":143,"score":49,"supported":42,"title":144,"abstract":145,"tags":147},"G1",[],[146],"成果转化",[],[149,150],"农业科技管理 中国农科院 建院70周年 土壤资源","农业科技管理 中国农科院","农业科技管理中国农科院建院70周年土壤资源-726",4,"2026-08-19T23:57:29.677085Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":8,"content":8,"source_name":159,"source_url":8,"published_at":160,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":164,"tags":166,"primary_topic":171,"secondary_topics":172,"classification":184,"search_phrases":206,"slug":209,"view_count":15,"doi":8,"paper":8,"created_at":210},331,"第十七届全国土壤肥料研究所管理与发展研讨会在内蒙古呼和浩特举行","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fnkyw\u002F4ed11f077aa44976891b204c29dfc1c1.htm","中国农业科学院8月4日发布,第十七届全国土壤肥料研究所管理与发展研讨会在内蒙古呼和浩特召开,会议围绕高标准农田建设、黑土地保护、盐碱地综合利用、第三次全国土壤普查成果应用、肥料减量增效与新型肥料研发等核心议题,系统交流各省土肥所\"十四五\"建设成效与\"十五五\"重点方向,推动全国土肥科技协作和成果转化落地。","中国农业科学院 2026-08-04","2026-08-04T00:00:00Z",71,{"impact":121,"substance":122,"depth":73,"authority":75,"freshness":77,"relevant":62,"comment":163},"全国性土肥科研协作会议，聚焦高标准农田与黑土地保护等核心议题，信息有实质内容，但为会议报道，深度一般，时效稍旧。",[165],{"name":159,"url":157},[167,168,21,169,170],"内蒙古","高标准农田","黑土地保护","土壤肥料",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[173,179],{"code":174,"slug":175,"name":176,"path":177,"parent_code":178},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"code":180,"slug":181,"name":182,"path":183,"parent_code":32},"H1","farmland","耕地保护与高标准农田","\u002Ftopic\u002Fenvironment\u002Ffarmland",{"status":35,"locked":14,"version":36,"method":37,"evidence":185},{"candidates":186,"agricultural_evidence":42},[187,192,197,202],{"code":28,"score":75,"supported":42,"title":188,"abstract":189,"tags":191},[44],[44,190],"盐碱地",[],{"code":174,"score":41,"supported":42,"title":193,"abstract":195,"tags":196},[194],"肥料",[194],[],{"code":180,"score":41,"supported":42,"title":198,"abstract":199,"tags":201},[],[168,169,200],"农田建设",[168,169],{"code":143,"score":49,"supported":42,"title":203,"abstract":204,"tags":205},[],[146],[],[207,208],"中国农科院 高标准农田 黑土地保护 土壤肥料","中国农科院 高标准农田","中国农科院高标准农田黑土地保护土壤肥料-331","2026-08-09T23:54:48.464469Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":216,"content":8,"source_name":217,"source_url":214,"published_at":218,"category":219,"cover_url":8,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":226,"tags":228,"primary_topic":234,"secondary_topics":235,"classification":242,"search_phrases":262,"slug":265,"view_count":15,"doi":266,"paper":267,"created_at":283},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","论文",64,{"impact":222,"substance":223,"depth":224,"authority":124,"freshness":13,"relevant":62,"comment":225},8,20,17,"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。",[227],{"name":217,"url":214},[229,230,231,232,233],"智慧农业","农业人工智能","主动学习","线虫检测","土壤采样",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[236],{"code":237,"slug":238,"name":239,"path":240,"parent_code":241},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":35,"locked":14,"version":36,"method":37,"evidence":243},{"candidates":244,"agricultural_evidence":42},[245,249,254],{"code":28,"score":75,"supported":42,"title":246,"abstract":247,"tags":248},[29],[44,29],[],{"code":237,"score":124,"supported":42,"title":250,"abstract":252,"tags":253},[251],"artificial intelligence",[],[230],{"code":255,"score":256,"supported":42,"title":257,"abstract":258,"tags":261},"I1",7,[],[259,260],"精准农业","precision agriculture",[229],[263,264],"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":266,"openalex_id":268,"authors":269,"venue":217,"cited_by_count":15,"oa_url":214,"card":276,"direction":280,"ingested_from":282},"W7223082195",[270,272,274],{"name":271,"orcid":8},"Amine Ben Salah",{"name":273,"orcid":8},"Ines Trabelsi",{"name":275,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":277,"method":278,"finding":279,"direction":280,"opportunity":281},"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。","openalex","2026-10-22T11:20:58.901747Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":8,"source_name":290,"source_url":287,"published_at":291,"category":219,"cover_url":8,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":295,"tags":297,"primary_topic":303,"secondary_topics":304,"classification":305,"search_phrases":331,"slug":334,"view_count":15,"doi":335,"paper":336,"created_at":369},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":74,"substance":121,"depth":122,"authority":75,"freshness":124,"relevant":62,"comment":294},"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。",[296],{"name":290,"url":287},[298,299,300,301,302],"遥感监测","莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[],{"status":35,"locked":14,"version":36,"method":37,"evidence":306},{"candidates":307,"agricultural_evidence":42},[308,312,319,326],{"code":28,"score":75,"supported":42,"title":309,"abstract":310,"tags":311},[29],[44,29],[],{"code":313,"score":256,"supported":42,"title":314,"abstract":315,"tags":318},"I3",[],[316,317],"遥感","remote sensing",[298],{"code":320,"score":61,"supported":42,"title":321,"abstract":322,"tags":325},"H7",[],[323,324],"气候适应","干旱",[],{"code":327,"score":62,"supported":14,"title":328,"abstract":329,"tags":330},"H6",[],[],[301],[332,333],"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":335,"openalex_id":337,"authors":338,"venue":290,"cited_by_count":15,"oa_url":287,"card":363,"direction":367,"ingested_from":282},"W7223069171",[339,341,343,345,348,350,353,356,358,360],{"name":340,"orcid":8},"Yue Li",{"name":342,"orcid":8},"Mengfei Shi",{"name":344,"orcid":8},"Fei Zhang",{"name":346,"orcid":347},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":349,"orcid":8},"Gowhar Meraj",{"name":351,"orcid":352},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":354,"orcid":355},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":357,"orcid":8},"Yuanfang Chai",{"name":359,"orcid":8},"Hongtao Xu",{"name":361,"orcid":362},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":364,"method":365,"finding":366,"direction":367,"opportunity":368},"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。","农业遥感与作物表型","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":371,"title":372,"url":373,"summary":374,"summary_zh":375,"content":8,"source_name":376,"source_url":373,"published_at":218,"category":219,"cover_url":8,"hotness":13,"is_selected":14,"score":119,"score_detail":377,"sources":380,"tags":382,"primary_topic":386,"secondary_topics":392,"classification":395,"search_phrases":429,"slug":432,"view_count":15,"doi":433,"paper":434,"created_at":452},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",{"impact":74,"substance":378,"depth":224,"authority":76,"freshness":13,"relevant":62,"comment":379},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。",[381],{"name":376,"url":373},[229,383,298,384,385],"机器学习","土壤墒情","冬小麦",{"code":387,"slug":388,"name":389,"path":390,"parent_code":391},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[393,394],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},{"code":237,"slug":238,"name":239,"path":240,"parent_code":241},{"status":35,"locked":14,"version":36,"method":37,"evidence":396},{"candidates":397,"agricultural_evidence":42},[398,404,408,413,418,425],{"code":387,"score":399,"supported":42,"title":400,"abstract":402,"tags":403},26,[401,388],"winter wheat",[389,401,388],[385],{"code":28,"score":123,"supported":42,"title":405,"abstract":406,"tags":407},[29],[44,29],[384],{"code":237,"score":123,"supported":42,"title":409,"abstract":411,"tags":412},[410],"machine learning",[383,410],[383],{"code":313,"score":13,"supported":42,"title":414,"abstract":415,"tags":417},[],[316,317,416],"multispectral",[298],{"code":419,"score":61,"supported":42,"title":420,"abstract":421,"tags":424},"H3",[],[422,423],"灌溉","irrigation",[],{"code":255,"score":152,"supported":42,"title":426,"abstract":427,"tags":428},[],[229],[229],[430,431],"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":433,"openalex_id":435,"authors":436,"venue":376,"cited_by_count":15,"oa_url":373,"card":446,"direction":451,"ingested_from":282},"W7223156204",[437,440,442,444],{"name":438,"orcid":439},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":441,"orcid":8},"Yi Zhang",{"name":443,"orcid":8},"WEI Lei",{"name":445,"orcid":8},"Chengcai Zhang",{"tldr":447,"method":448,"finding":449,"direction":367,"opportunity":450},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z"]