[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2770":3,"related-2770":133},{"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":33,"secondary_topics":38,"classification":39,"search_phrases":107,"slug":110,"view_count":22,"doi":111,"paper":112,"created_at":132},2770,"Science and technology for food system transformation: Integrating edible biodiversity, grassland conservation, nutrition, and climate resilience in Ghana and Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jafr.2026.103302","ABSTRACT Grassland ecosystems in Ghana and comparable African savanna regions support diverse plant resources that contribute to food security, ecosystem functioning, and climate adaptation. However, many edible plant species remain underutilized due to limited scientific characterization, weak integration into food systems, inadequate conservation attention, and insufficient application of emerging technologies for biodiversity assessment and management. This review evaluates the role of science and technology in supporting edible biodiversity conservation, nutritional utilization, grassland monitoring, and climate resilience within African grassland contexts. The review further examines processing technologies, nutritional composition, and food-safety considerations for underutilized edible species, linking conservation outcomes to food manufacturing applications and dietary relevance. A structured narrative review was conducted using records retrieved from Web of Science and Google Scholar, covering studies published from 2000 to 2026. Following the PRISMA-based identification, screening, eligibility assessment, and inclusion process, 74 studies were selected for qualitative synthesis. The included literature was examined across three interconnected thematic domains: (i) science and technology applications, including remote sensing, geographic information systems, ecological monitoring approaches, and molecular techniques; (ii) edible biodiversity conservation and utilization, focusing on plant diversity, nutritional potential, indigenous food resources, and sustainable management; and (iii) climate resilience and policy integration, addressing ecosystem adaptation, governance, and conservation planning. The review demonstrates that technological approaches provide valuable tools for documenting biodiversity patterns, improving ecological assessments, and strengthening evidence-based conservation decisions, although the extent of their application remains uneven across African grassland systems. The findings further highlight that underutilized edible species are constrained not only by limited consumption and market integration but also by inadequate agronomic research, conservation prioritization, and policy recognition. Based on the synthesized evidence, a science–technology integration framework is proposed to connect biodiversity monitoring, nutritional assessment, sustainable utilization, and climate adaptation strategies. This framework provides an evidence-informed pathway for advancing the conservation and sustainable use of edible plant diversity. The framework also recognizes the current limitations in empirical validation and implementation across different grassland regions.","摘要 加纳及类似非洲稀树草原地区的草地生态系统支撑着多样化的植物资源，这些资源对粮食安全、生态系统功能和气候适应具有重要贡献。然而，许多可食用植物物种仍未得到充分利用，原因包括科学表征有限、与粮食系统的整合薄弱、保护关注不足，以及新兴技术在生物多样性评估和管理中的应用不充分。本综述评估了科学技术在非洲草地背景下支持可食用生物多样性保护、营养利用、草地监测和气候韧性方面的作用。综述进一步考察了未充分利用可食用物种的加工技术、营养成分和食品安全考量，将保护成果与食品制造应用及膳食相关性联系起来。本研究采用结构化叙述性综述方法，使用从Web of Science和Google Scholar检索的记录，涵盖2000年至2026年发表的研究。遵循基于PRISMA的识别、筛选、资格评估和纳入流程，最终选取74项研究进行定性综合。纳入文献在三个相互关联的主题领域进行了考察：(i) 科学技术应用，包括遥感、地理信息系统、生态监测方法和分子技术；(ii) 可食用生物多样性保护与利用，聚焦植物多样性、营养潜力、本土食物资源和可持续管理；(iii) 气候韧性与政策整合，涉及生态系统适应、治理和保护规划。综述表明，技术方法为记录生物多样性模式、改善生态评估和加强基于证据的保护决策提供了有价值的工具，尽管其在非洲草地系统中的应用程序仍不均衡。研究结果进一步强调，未充分利用的可食用物种不仅受到消费和市场整合有限的制约，还受到农艺研究不足、保护优先度低和政策认可不够的限制。基于综合证据，提出了一个科学技术整合框架，以连接生物多样性监测、营养评估、可持续利用和气候适应策略。该框架为",null,"Journal of Agriculture and Food Research","2026-09-16T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"基于74项研究的PRISMA综述，提出科技整合框架，对非洲草原可食用生物多样性保护与利用具参考价值，但属区域外研究、应用落地尚不均衡。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","气候韧性","生物多样性","遥感监测","营养安全","草原保护",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},"H","environment","资源环境与气候变化","\u002Ftopic\u002Fenvironment",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"partial","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":48,"reason":106},[45,53,60,68,76,83,90,96,101],{"code":46,"score":47,"supported":48,"title":49,"abstract":51,"tags":52},"H6",15,true,[50],"biodiversity",[29,50],[29],{"code":54,"score":47,"supported":48,"title":55,"abstract":57,"tags":59},"H7",[56],"climate resilience",[58,56],"气候适应",[28],{"code":61,"score":62,"supported":48,"title":63,"abstract":64,"tags":67},"I3",7,[],[65,66],"遥感","remote sensing",[30],{"code":69,"score":70,"supported":48,"title":71,"abstract":72,"tags":75},"A8",6,[],[73,74],"粮食安全","food security",[],{"code":77,"score":70,"supported":48,"title":78,"abstract":79,"tags":82},"I4",[],[80,81],"地理信息","geographic information",[],{"code":84,"score":85,"supported":48,"title":86,"abstract":87,"tags":89},"E1",3,[],[88],"food manufacturing",[],{"code":91,"score":85,"supported":48,"title":92,"abstract":93,"tags":95},"E4",[],[94],"食品安全",[],{"code":97,"score":22,"supported":14,"title":98,"abstract":99,"tags":100},"E2",[],[],[31],{"code":102,"score":22,"supported":14,"title":103,"abstract":104,"tags":105},"I1",[],[],[27],"一级领域明确，二级方向需要细分",[108,109],"生物多样性 智慧农业 气候韧性 草原保护","生物多样性 智慧农业","生物多样性智慧农业气候韧性草原保护-2770","10.1016\u002Fj.jafr.2026.103302",{"doi":111,"openalex_id":113,"authors":114,"venue":10,"cited_by_count":122,"oa_url":123,"card":124,"direction":130,"ingested_from":131},"W7213261782",[115,117,120],{"name":116,"orcid":9},"Kwame Anokye",{"name":118,"orcid":119},"Huang Rong","https:\u002F\u002Forcid.org\u002F0000-0003-1039-0914",{"name":121,"orcid":9},"Zhen-fen Zhang",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666154326006733\u002Fpdf",{"tldr":125,"method":126,"finding":127,"direction":128,"opportunity":129},"综述非洲草地可食用生物多样性保护与利用，提出科技整合框架连接监测、营养与气候适应。","PRISMA系统综述，检索Web of Science和Google Schol","技术应用不均衡，未充分利用的可食用物种受农艺研究、保护优先和政策认可不足制约。","农业绿色发展与碳","可研究遥感与分子技术如何具体支撑非洲草地可食用物种的营养评估与保护决策。","智慧农业 \u002F 农业物联网","openalex","2026-09-17T23:30:10.652175Z",{"total":70,"page":22,"page_size":70,"items":134},[135,228,312,391,468,528],{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":9,"source_name":141,"source_url":138,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":148,"tags":150,"primary_topic":155,"secondary_topics":156,"classification":157,"search_phrases":192,"slug":195,"view_count":196,"doi":197,"paper":198,"created_at":227},3348,"Nanobubble Technology in Smart Agriculture: Underlying Mechanisms and Application Dynamics for Resource Optimization","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41101-026-00573-2","Nanobubble (NB) technology is considered as an innovative approach in terms of efficient utilization of resources and production of crops. A nanobubble is an ultra-fine gas bubble generally smaller than 1 μm in diameter, commonly reported in the range of 50–200 nm, which behaves differently from ordinary bubbles due to its apparently tiny size, high internal pressure and surface charge. Their applications include irrigation management through improved water and oxygen delivery to the root zone, soil management by enhancing soil structure and aeration, and nutrient management with improved nutrient uptake and favourable microbial interactions. Despite these advances, large scale adoption remains constrained by greater variability in water quality, soil heterogeneity, irrigation system design and limitations related to nanobubble generation efficiency and economic feasibility. Addressing these challenges requires coordinated interdisciplinary efforts integrating agronomy, soil science and engineering to optimize application strategies and scalability. This review aims to synthesize current knowledge on nano bubble technology in agriculture, applications in irrigation, soil, nutrient, microbial management and assess its integration with sustainable systems. However, much of the available evidence is based on short-term or controlled studies and further long-term field validation across diverse agroecological conditions is needed. Together, nanobubble technology holds a promising future for maintaining and enhancing sustainability in agriculture. Graphical Abstract","纳米气泡（NB）技术被认为是一种在资源高效利用和作物生产方面的创新方法。纳米气泡是一种超细气泡，直径通常小于1 μm，常见报道范围为50–200 nm，由于其极小的尺寸、高内压和表面电荷，其行为与普通气泡不同。其应用包括通过改善水分和氧气向根区的输送来进行灌溉管理，通过增强土壤结构和通气性来进行土壤管理，以及通过改善养分吸收和有利的微生物相互作用来进行养分管理。尽管取得了这些进展，但大规模采用仍受限于水质变异性较大、土壤异质性、灌溉系统设计以及纳米气泡生成效率和经济可行性方面的局限。应对这些挑战需要协调一致的跨学科努力，整合农学、土壤科学和工程学，以优化应用策略和可扩展性。本综述旨在综合当前关于纳米气泡技术在农业中应用的知识，包括在灌溉、土壤、养分和微生物管理中的应用，并评估其与可持续系统的整合。然而，现有证据大多基于短期或受控研究，仍需在不同农业生态条件下进行进一步的长期田间验证。总之，纳米气泡技术在维持和增强农业可持续性方面具有广阔前景。图形摘要","Water Conservation Science and Engineering","2026-09-23T00:00:00Z",76,{"impact":145,"substance":146,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":147},18,20,"系统综述纳米气泡技术在灌溉、土壤与养分管理中的应用机制与规模化瓶颈，属智慧农业资源优化方向的前沿论文，但偏理论综述、缺乏长期田间验证，适合主题聚合而非头条精选。",[149],{"name":141,"url":138},[27,151,152,153,154],"水肥一体化","土壤健康","节水灌溉","纳米气泡",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":158},{"candidates":159,"agricultural_evidence":48,"reason":106},[160,166,173,180,187],{"code":102,"score":161,"supported":48,"title":162,"abstract":164,"tags":165},9,[163],"smart agriculture",[],[27],{"code":167,"score":62,"supported":48,"title":168,"abstract":169,"tags":172},"H2",[],[170,171],"土壤","soil",[152],{"code":174,"score":62,"supported":48,"title":175,"abstract":176,"tags":179},"H3",[],[177,178],"灌溉","irrigation",[153],{"code":181,"score":182,"supported":48,"title":183,"abstract":184,"tags":186},"F3",4,[],[185],"养分管理",[151],{"code":46,"score":85,"supported":48,"title":188,"abstract":189,"tags":191},[],[190],"农业生态",[],[193,194],"纳米气泡 灌溉","水肥一体化 土壤健康 智慧农业 纳米气泡","纳米气泡灌溉-3348",2,"10.1007\u002Fs41101-026-00573-2",{"doi":197,"openalex_id":199,"authors":200,"venue":141,"cited_by_count":122,"oa_url":138,"card":222,"direction":130,"ingested_from":131},"W7214068919",[201,204,207,210,213,216,219],{"name":202,"orcid":203},"Deepasri K","https:\u002F\u002Forcid.org\u002F0009-0005-6635-5378",{"name":205,"orcid":206},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"name":208,"orcid":209},"P Kathirvelan","https:\u002F\u002Forcid.org\u002F0000-0003-3576-3545",{"name":211,"orcid":212},"Sathiya Bama Kaliappan","https:\u002F\u002Forcid.org\u002F0000-0001-8752-7881",{"name":214,"orcid":215},"P C Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":217,"orcid":218},"R. Kuttimani","https:\u002F\u002Forcid.org\u002F0000-0002-1067-3597",{"name":220,"orcid":221},"M. N. Karthik","https:\u002F\u002Forcid.org\u002F0009-0003-4662-3078",{"tldr":223,"method":224,"finding":225,"direction":130,"opportunity":226},"综述纳米气泡技术在智能农业中优化水、土、养分资源的作用机制与应用前景。","综述纳米气泡在灌溉、土壤、养分和微生物管理中的应用及机制。","纳米气泡可提升水氧输送、土壤通气与养分吸收，但规模化受水质、土壤异质性和成本限制。","可研究纳米气泡与物联网传感器结合，实现灌溉水气精准调控及长期田间验证。","2026-09-24T23:30:09.954106Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":234,"source_url":231,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":246,"secondary_topics":247,"classification":248,"search_phrases":286,"slug":289,"view_count":85,"doi":290,"paper":291,"created_at":311},2414,"AgroVaani: Vernacular Generative Intelligence for Zero-Barrier Precision Agriculture Advisory Using Multilingual RAG-LLMs","https:\u002F\u002Fdoi.org\u002F10.64643\u002Fijirt.208380-459","Farming today is becoming more challenging because of unpredictable weather, worsening soil health, more frequent pest and disease outbreaks, and limited access to expert advice when farmers need it most.While traditional agricultural advisory services have helped farmers for many years, they often fall short in giving advice that suits each farmer's unique location and situation.This is due to not having enough experts, language differences, and varied farming practices across regions.To ad-dress these issues, this research introduces a Smart Farming Assistant Chatbot powered by advanced Al technologies like Generative Al, Large Language Models, Natural Language Processing, and Retrieval-Augmented Generation.This chatbot is designed to be multilingual and voice-enabled, so farmers can easily talk to it in their own language or dialect.It supports many regional languages including Marathi, Hindi, Kan-nada, Tamil, Telugu, Bengali, Gujarati, Punjabi, Malayalam, Odia, Assamese, and English.Unlike typical chatbots that use fixed answers, this assistant understands natural conversations, local farming terms, and even mixed language use like Marathi-English or Hindi-English, making communication smooth and natural.The system brings together a wide range of farming information-from details about crops, soil, and weather, to pest control, fertilizers, market trends, and government schemes.By combining the power of Al with trustworthy agricultural knowledge, the chatbot offers practical, ac-curate, and personalized advice while minimizing the chances of giving wrong information.What makes this system truly special is that it breaks down language barriers, allowing farmers to get expert-level help without needing technical skills or knowing a specific language.Farmers can interact with it through text or voice, getting support for everything from choosing crops and planning irrigation, to managing soil, preventing diseases, and making sustainable farming decisions.This approach aims to empower small and marginal farmers, reduce their reliance on traditional advisory ser-vices, help them use resources more efficiently, and promote smart, inclusive farming that benefits everyone.Ultimately, this system moves us closer to an Al-driven agricultural world where technology adapts to farmers' needs, making farming easier and more productive for all.","当今农业面临着日益严峻的挑战，包括不可预测的天气、不断恶化的土壤健康、愈发频繁的病虫害暴发，以及农民在最需要时难以获得专家指导。尽管传统农业咨询服务多年来为农民提供了帮助，但往往难以给出适合每位农民特定地理位置和实际情况的建议。这源于专家数量不足、语言差异以及各地区耕作方式的不同。为解决这些问题，本研究提出了一款智能农业助手聊天机器人，由生成式人工智能（Generative AI）、大语言模型（Large Language Models）、自然语言处理（Natural Language Processing）和检索增强生成（Retrieval-Augmented Generation）等先进人工智能技术驱动。该聊天机器人设计为多语言和语音交互模式，农民可以用自己的语言或方言轻松与之对话。它支持多种区域语言，包括马拉地语、印地语、卡纳达语、泰米尔语、泰卢固语、孟加拉语、古吉拉特语、旁遮普语、马拉雅拉姆语、奥里亚语、阿萨姆语和英语。与使用固定答案的典型聊天机器人不同，该助手能够理解自然对话、地方农业术语，甚至马拉地语-英语或印地语-英语等混合语言使用，使交流顺畅自然。该系统汇集了广泛的农业信息——从作物、土壤和天气的详细信息，到病虫害防治、肥料、市场趋势和政府计划。通过将人工智能的能力与可靠的农业知识相结合，该聊天机器人提供实用、准确且个性化的建议，同时最大限度地降低给出错误信息的可能性。该系统的真正特别之处在于它打破了语言障碍，使农民无需技术技能或掌握特定语言即可获得专家级的帮助。农民可以通过文字或语音与之互动，获得从选择作物、规划灌溉，到管理土壤、预防疾病和做出可持续农业决策等各方面的支持。该方法旨在赋能小农户和边缘农户，减少他们对传统咨询服务的依赖，帮助他们更高效地利用资源，并促进惠及所有人的智能、包容性农业。最终，该系统推动我们更接近一个人工智能驱动的农业世界，在那里技术适应农民的需求，使农业对所有人来说都更轻松、更高效。","International Journal of Innovative Research in Technology","2026-09-11T00:00:00Z",69,{"impact":145,"substance":146,"depth":19,"authority":70,"freshness":21,"relevant":22,"comment":238},"面向印度多语种农户的RAG-LLM语音农业问答系统，方法组合与多语言覆盖有实质增量，但期刊权威性一般，属细分领域技术进展。",[240],{"name":234,"url":231},[27,242,243,244,245],"农业人工智能","多语言大模型","农业问答机器人","小农户服务",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":249},{"candidates":250,"agricultural_evidence":48,"reason":106},[251,256,260,264,270,275,281],{"code":102,"score":161,"supported":48,"title":252,"abstract":254,"tags":255},[253],"precision agriculture",[],[27],{"code":167,"score":70,"supported":48,"title":257,"abstract":258,"tags":259},[],[170,171],[],{"code":174,"score":70,"supported":48,"title":261,"abstract":262,"tags":263},[],[177,178],[],{"code":265,"score":182,"supported":48,"title":266,"abstract":267,"tags":269},"G2",[],[268],"小农户",[245],{"code":181,"score":85,"supported":48,"title":271,"abstract":272,"tags":274},[],[273],"肥料",[],{"code":276,"score":85,"supported":48,"title":277,"abstract":278,"tags":280},"H8",[],[279],"可持续农业",[],{"code":282,"score":22,"supported":14,"title":283,"abstract":284,"tags":285},"I2",[],[],[242],[287,288],"农业问答机器人 农业人工智能 多语言大模型 小农户服务","农业问答机器人 农业人工智能","农业问答机器人农业人工智能多语言大模型小农户服务-2414","10.64643\u002Fijirt.208380-459",{"doi":290,"openalex_id":292,"authors":293,"venue":234,"cited_by_count":122,"oa_url":304,"card":305,"direction":130,"ingested_from":131},"W7212237513",[294,296,298,300,302],{"name":295,"orcid":9},"Devansh Adik Wable",{"name":297,"orcid":9},"Sahil Balshiram Pawar",{"name":299,"orcid":9},"Shravani Chetan Kalid",{"name":301,"orcid":9},"Sanjay Nathu Bombale",{"name":303,"orcid":9},"Mayuri Jayesh Patil -","https:\u002F\u002Fijirt.org\u002Fpublishedpaper\u002FIJIRT208380_PAPER.pdf",{"tldr":306,"method":307,"finding":308,"direction":309,"opportunity":310},"构建多语言语音农业咨询聊天机器人，用RAG-LLM为农民提供零障碍精准建议。","生成式AI、大语言模型、NLP与检索增强生成，支持12种印度语言及混合语。","系统能理解本地农耕术语与混合语言，提供个性化、可信的农业建议。","数字乡村与农业信息化","可探索低资源方言的RAG知识库构建与幻觉抑制，并评估对农户决策的实际影响。","2026-09-14T23:30:09.870436Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":9,"source_name":318,"source_url":315,"published_at":319,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":320,"score_detail":321,"sources":324,"tags":326,"primary_topic":331,"secondary_topics":332,"classification":333,"search_phrases":364,"slug":367,"view_count":85,"doi":368,"paper":369,"created_at":390},1751,"Downscaling SMAP Soil Moisture to 1 km with Machine Learning and MODIS Data for Agricultural Drought Assessment in Békés County, Hungary","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8090373","Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R2 = 0.71, RMSE = 0.0295 m3\u002Fm3) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling.","土壤水分（SM）的精确制图对于有效监测农业干旱至关重要。然而，被动微波产品（包括9公里分辨率的土壤水分主动被动（SMAP）反演数据）空间分辨率较粗，限制了其在区域和地方尺度上的有效性。为解决这一局限，本研究比较了三种基于机器学习的降尺度框架，旨在将匈牙利贝凯什县的SMAP土壤水分分辨率从9公里提升至1公里。研究时段覆盖2020年至2023年的生长季（4月至10月）。研究中将一组多时相MODIS衍生变量——包括植被指数（NDVI、EVI）、白天和夜间地表温度、蒸散量——以及土地覆盖分类和地形高程作为辅助预测变量。三种机器学习算法，即随机森林（RF）、极限梯度提升（XGBoost）和梯度提升机（GBM），被训练和评估。结果表明：（1）RF模型在测试阶段（R² = 0.71，RMSE = 0.0295 m³\u002Fm³）精度最高，且经四个原位监测站验证，确认其在小尺度上具有可靠的土壤水分估算能力；（2）白天地表温度是所有模型中最重要的预测变量，凸显了控制地表土壤水分动态的强烈热-湿耦合关系；（3）经过验证的RF模型生成的1公里标准化土壤水分指数（SSI）图有效捕捉了年际干旱变率，识别出2022年7月的严重干旱事件。总体而言，本研究提出了一种适用于中欧农业环境的降尺度方法，用于生成高分辨率土壤水分数据。所得到的1公里土壤水分和SSI产品为决策者在干旱期加强规划、通过改进灌溉调度减少农业损失提供了有价值的工具。","AgriEngineering","2026-09-04T00:00:00Z",66,{"impact":322,"substance":146,"depth":145,"authority":322,"freshness":182,"relevant":22,"comment":323},12,"研究利用机器学习将SMAP土壤水分降尺度至1km，提升农业干旱监测精度，方法新颖，数据详实，对区域农业管理有参考价值。",[325],{"name":318,"url":315},[27,327,328,329,330],"农业遥感","机器学习","干旱监测","土壤水分",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":334},{"candidates":335,"agricultural_evidence":48,"reason":106},[336,340,346,351,356,360],{"code":167,"score":47,"supported":48,"title":337,"abstract":338,"tags":339},[171],[170,171],[330],{"code":54,"score":47,"supported":48,"title":341,"abstract":343,"tags":345},[342],"drought",[344,342],"干旱",[329],{"code":282,"score":47,"supported":48,"title":347,"abstract":349,"tags":350},[348],"machine learning",[328,348],[328],{"code":174,"score":161,"supported":48,"title":352,"abstract":353,"tags":355},[],[177,178,354],"evapotranspiration",[],{"code":102,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[27],{"code":61,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[327],[365,366],"农业遥感 土壤水分 干旱监测 智慧农业","农业遥感 土壤水分","农业遥感土壤水分干旱监测智慧农业-1751","10.3390\u002Fagriengineering8090373",{"doi":368,"openalex_id":370,"authors":371,"venue":318,"cited_by_count":122,"oa_url":315,"card":383,"direction":389,"ingested_from":131},"W7208719644",[372,374,377,380],{"name":373,"orcid":9},"Mahrokh Shafiei",{"name":375,"orcid":376},"István Waltner","https:\u002F\u002Forcid.org\u002F0000-0002-6704-8936",{"name":378,"orcid":379},"Z. Vekerdy","https:\u002F\u002Forcid.org\u002F0000-0002-5677-8298",{"name":381,"orcid":382},"Gábor Halupka","https:\u002F\u002Forcid.org\u002F0000-0003-4640-0061",{"tldr":384,"method":385,"finding":386,"direction":387,"opportunity":388},"用机器学习将SMAP土壤水分降尺度至1km，评估匈牙利农业干旱。","比较RF、XGBoost、GBM，结合MODIS植被指数、地表温度等预测变量。","RF精度最高，白天LST最重要，成功捕捉2022年7月严重干旱。","农业遥感与作物表型","可探索将降尺度方法应用于其他区域或作物，结合更高分辨率遥感或气象数据提升精度。","农业人工智能与决策模型","2026-09-05T23:30:50.054057Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":397,"source_url":394,"published_at":398,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":403,"tags":405,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":430,"slug":433,"view_count":196,"doi":434,"paper":435,"created_at":467},1433,"Field-scale irrigation estimation using Sentinel-1 and Sentinel-2 data within a soil water balance model coupled to the Water Cloud Model","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rse.2026.115639","Irrigation is a significant component of the water cycle in agricultural regions: however, information about how much and where irrigation is applied is scarce and uncertain. Remote sensing-based products have been developed to retrieve irrigation across scales, but they exhibit limitations due to spatial uncertainties and confounding factors, particularly in highly fragmented agricultural landscapes. This paper presents a novel method to estimate irrigation at the field scale by integrating Sentinel-1 C-band Synthetic Aperture Radar (SAR) backscatter data and Sentinel-2 NDVI data within a modeling framework. The proposed approach involves the joint calibration of the Water Cloud Model (WCM) and a two-layer Soil Water Balance (SWB) model to simultaneously simulate backscatter, irrigation, surface and root-zone soil moisture, and other water balance components. The model is driven by reanalysis meteorological data, and selected parameters in the WCM and SWB are calibrated exclusively using Sentinel-1 backscatter data, thus making the framework independent of in situ ground measurements. The framework is tested on 38 fields within an irrigation district in the Po Valley, Italy, during 2018, for which irrigation data are available as reference. It is shown that the capability to estimate irrigation events depends on the treatment of the SAR data: after bias-correcting Sentinel-1 observations, average time series correlations of 0.43 to 0.95 could be obtained for daily to bi-weekly irrigation estimates, respectively. The associated bias amounts to 0.01 mm\u002Fday and 0.04 mm\u002F15 days, and less than 1% for the annual estimates.","灌溉是农业区域水循环的重要组成部分，然而关于灌溉量及其分布的信息却稀缺且不确定。基于遥感的产品已被开发用于跨尺度反演灌溉信息，但由于空间不确定性和混杂因素，特别是在高度破碎化的农业景观中，这些产品表现出局限性。本文提出了一种新方法，通过将Sentinel-1 C波段合成孔径雷达（SAR）后向散射数据与Sentinel-2 NDVI数据整合在一个建模框架内，实现田块尺度上的灌溉估算。该方法涉及水云模型（WCM）与两层土壤水平衡（SWB）模型的联合校准，以同时模拟后向散射、灌溉量、地表及根区土壤湿度以及其他水平衡组分。模型由再分析气象数据驱动，且WCM和SWB中的选定参数仅使用Sentinel-1后向散射数据进行校准，从而使该框架不依赖于实地地面测量。该框架在意大利波河流域一个灌区的38块田地上进行了测试，时间为2018年，并提供了相应的灌溉数据作为参考。研究表明，估算灌溉事件的能力取决于对SAR数据的处理方式：在对Sentinel-1观测值进行偏差校正后，日尺度至双周尺度的灌溉估算平均时间序列相关系数分别可达0.43至0.95。相应的偏差分别为0.01毫米\u002F天和0.04毫米\u002F15天，年度估算的偏差则小于1%。","Remote Sensing of Environment","2026-09-01T00:00:00Z",82,{"impact":145,"substance":401,"depth":146,"authority":47,"freshness":62,"relevant":22,"comment":402},22,"提出结合Sentinel-1\u002F2与土壤水平衡模型的田间尺度灌溉估算新方法，无需地面实测，精度高，对农业水资源管理有重要价值。",[404],{"name":397,"url":394},[27,65,406,407],"灌溉估算","Sentinel",{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":411},{"candidates":412,"agricultural_evidence":48,"reason":106},[413,418,422,426],{"code":167,"score":414,"supported":48,"title":415,"abstract":416,"tags":417},14,[171],[170,171],[],{"code":174,"score":414,"supported":48,"title":419,"abstract":420,"tags":421},[178],[177,178],[],{"code":61,"score":62,"supported":48,"title":423,"abstract":424,"tags":425},[],[65,66],[65],{"code":102,"score":22,"supported":14,"title":427,"abstract":428,"tags":429},[],[],[27],[431,432],"智慧农业 灌溉估算 遥感 Sentinel","智慧农业 灌溉估算","智慧农业灌溉估算遥感Sentinel-1433","10.1016\u002Fj.rse.2026.115639",{"doi":434,"openalex_id":436,"authors":437,"venue":397,"cited_by_count":122,"oa_url":394,"card":462,"direction":387,"ingested_from":131},"W4415587276",[438,441,444,447,450,453,456,459],{"name":439,"orcid":440},"Martina Natali","https:\u002F\u002Forcid.org\u002F0009-0007-5466-686X",{"name":442,"orcid":443},"Sara Modanesi","https:\u002F\u002Forcid.org\u002F0000-0003-4720-5233",{"name":445,"orcid":446},"Domenico De Santis","https:\u002F\u002Forcid.org\u002F0000-0003-0267-0078",{"name":448,"orcid":449},"Gabriëlle De Lannoy","https:\u002F\u002Forcid.org\u002F0000-0002-6743-7122",{"name":451,"orcid":452},"Susan Steele‐Dunne","https:\u002F\u002Forcid.org\u002F0000-0002-8644-3077",{"name":454,"orcid":455},"Alessia Flammini","https:\u002F\u002Forcid.org\u002F0000-0002-9823-3413",{"name":457,"orcid":458},"Alessio Domeneghetti","https:\u002F\u002Forcid.org\u002F0000-0003-4726-5316",{"name":460,"orcid":461},"Christian Massari","https:\u002F\u002Forcid.org\u002F0000-0003-0983-1276",{"tldr":463,"method":464,"finding":465,"direction":387,"opportunity":466},"提出一种仅用Sentinel-1和Sentinel-2数据估算田间灌溉量的方法，无需地面实测。","耦合水云模型和两层土壤水平衡模型，联合校准，用Sentinel-1后向散射数据驱","偏差校正后，日到双周灌溉估算相关系数0.43-0.95，年估算偏差小于1%。","可探索在更破碎景观或不同作物下扩展该方法，或结合高分辨率土壤湿度产品提高估算精度。","2026-09-02T23:30:51.202761Z",{"id":469,"title":470,"url":471,"summary":472,"summary_zh":473,"content":9,"source_name":474,"source_url":471,"published_at":475,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":476,"sources":478,"tags":480,"primary_topic":481,"secondary_topics":482,"classification":483,"search_phrases":513,"slug":514,"view_count":122,"doi":515,"paper":516,"created_at":527},1304,"Digital Twins for Sustainable Groundwater Resources Management: From Monitoring and Prediction to Governance and Resilience—A Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fhydrology13090236","This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts.","本文对数字孪生（DTs）在可持续地下水资源管理中的应用文献进行了范围综述，该领域是一个极为新兴且快速扩展的研究方向，相关文献正迅速从概念框架转向面向应用的系统。通过PRISMA 2020方法筛选出的文献被划分为七个子主题：人工智能与机器学习基础、数字孪生架构与框架、含水层尺度数字孪生应用、农业及水-能源-粮食（WEF）关联数字孪生、流域与城市水数字孪生、传感监测与物联网基础设施，以及治理、韧性与社会水文学。这一结构揭示了该领域正从监测与预测向综合决策支持转变，其中过程模型、机器学习替代模型、实时传感与优化相结合，以支持干旱缓解、海水入侵控制、灌溉管理、气候适应及流域尺度规划。在综述的研究中，最常出现的贡献包括混合模型架构的构建、利用数字孪生实现观测与控制之间的闭环，以及日益认识到地下水管理必须纳入治理、利益相关者决策和社会水文反馈。与此同时，现有文献仍面临关键局限，尤其是不确定性量化、模型与数据流之间的互操作性、向数据稀缺环境迁移的能力，以及在实际运行条件下验证的不足。因此，未来研究应聚焦于物理信息驱动和可解释的人工智能、联邦式与可扩展的数字孪生架构、与社会水文及治理框架的更紧密耦合，以及更多经过现场测试的实施案例，以证明其在不同水文地质和制度背景下的稳健性能。","Hydrology","2026-08-31T00:00:00Z",{"impact":122,"substance":122,"depth":122,"authority":122,"freshness":122,"relevant":122,"comment":477},"主题为地下水管理数字孪生，不直接涉及三农或农业信息化，相关性不足。",[479],{"name":474,"url":471},[27],{"code":34,"slug":35,"name":36,"path":37,"parent_code":9},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":484},{"candidates":485,"agricultural_evidence":48,"reason":106},[486,491,495,500,506],{"code":174,"score":161,"supported":48,"title":487,"abstract":488,"tags":490},[],[177,489,178],"水资源",[],{"code":54,"score":161,"supported":48,"title":492,"abstract":493,"tags":494},[],[58,344,342],[],{"code":282,"score":161,"supported":48,"title":496,"abstract":497,"tags":499},[],[328,348,498],"decision support",[],{"code":102,"score":62,"supported":48,"title":501,"abstract":502,"tags":505},[],[503,504],"数字孪生","digital twin",[27],{"code":507,"score":70,"supported":48,"title":508,"abstract":509,"tags":512},"I5",[],[510,511],"物联网","IoT",[],[],"DigitalTwinsforSustainableGroundwaterRes-1304","10.3390\u002Fhydrology13090236",{"doi":515,"openalex_id":517,"authors":518,"venue":474,"cited_by_count":122,"oa_url":471,"card":522,"direction":130,"ingested_from":131},"W7204769003",[519],{"name":520,"orcid":521},"Iolanda Borzì","https:\u002F\u002Forcid.org\u002F0000-0002-9847-8699",{"tldr":523,"method":524,"finding":525,"direction":389,"opportunity":526},"综述数字孪生在地下水可持续管理中的应用，从监测预测到治理韧性。","PRISMA 2020方法筛选文献，按七个子主题分类综述。","数字孪生正从监测预测转向综合决策支持，但存在不确定性量化等局限。","可研究物理信息可解释AI、联邦架构及与社会水文耦合的田间验证，提升数字孪生鲁棒性。","2026-09-01T23:30:12.737119Z",{"id":529,"title":530,"url":531,"summary":532,"summary_zh":533,"content":9,"source_name":534,"source_url":531,"published_at":535,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":536,"score_detail":537,"sources":539,"tags":541,"primary_topic":544,"secondary_topics":547,"classification":548,"search_phrases":561,"slug":564,"view_count":122,"doi":565,"paper":566,"created_at":580},4821,"Producing a Red List of Ecosystems for the Brazilian Cerrado","https:\u002F\u002Fdoi.org\u002F10.1111\u002Fcsp2.70426","Abstract Land clearance is a key driver of biodiversity loss. This is a particular challenge in the Brazilian Cerrado, the world's most biodiverse savanna biome and a region experiencing a rapid expansion of agriculture. To inform habitat conservation and restoration actions, we used the Red List of Ecosystems (RLE) framework to identify threatened ecosystems within the Cerrado. This biome is divided into 20 ecoregions, and we subdivided each ecoregion into forest, savanna, grassland, and wetland ecosystems. We used remote sensing data from 1985 to 2020 to measure and predict the distribution of each ecosystem and assess its risk of collapse based on Criterion A2b (reduction in geographic distribution over any 50‐year period), Criterion B1 (limited extent of occurrence), and Criterion B2 (limited area of occupancy). Of the 80 ecosystems comprising the Cerrado, we assessed 12 as Endangered and 43 as Vulnerable. Most of these threatened ecosystems were identified under Criterion A2b, while Criterion B identified a smaller subset of spatially restricted ecosystems, particularly wetlands, underscoring the importance of applying more than one criterion in the assessment process. By using the Red List of Ecosystems as an international standard, we provide the first globally comparable analysis of the state of the Cerrado, showing that nearly three‐quarters of the region's ecosystems are at risk of collapse. This information provides a baseline to inform local policy and practice, helping to conserve biodiversity and maintain ecosystem services within this globally important region.","土地开垦是生物多样性丧失的主要驱动因素。这在巴西塞拉多（Cerrado）尤为严峻，该地区拥有全球生物多样性最丰富的稀树草原生物群系，同时正经历农业的快速扩张。为了为栖息地保护和恢复行动提供依据，我们采用生态系统红色名录（Red List of Ecosystems, RLE）框架来识别塞拉多境内受威胁的生态系统。该生物群系被划分为20个生态区，我们又将每个生态区细分为森林、稀树草原、草地和湿地生态系统。我们利用1985年至2020年的遥感数据，测量并预测各生态系统的分布，并基于标准A2b（任意50年期内地理分布范围的缩减）、标准B1（分布范围有限）和标准B2（占据面积有限）评估其崩溃风险。在构成塞拉多的80个生态系统中，我们评估出12个为濒危，43个为易危。这些受威胁的生态系统大多依据标准A2b被识别，而标准B则识别出较少一部分空间受限的生态系统，尤其是湿地，这凸显了在评估过程中应用多项标准的重要性。通过将生态系统红色名录作为国际标准加以运用，我们提供了首个在全球范围内可比较的塞拉多状况分析，表明该地区近四分之三的生态系统面临崩溃风险。这些信息为地方政策和实践提供了基线，有助于在这一具有全球重要性的区域内保护生物多样性并维持生态系统服务。","Conservation Science and Practice","2026-10-12T00:00:00Z",81,{"impact":145,"substance":401,"depth":145,"authority":414,"freshness":161,"relevant":22,"comment":538},"基于1985—2020年遥感数据对巴西塞拉多80个生态系统做红色名录评估，方法规范、数据扎实，对农业扩张与生态保护平衡有参考价值，但属域外研究，国内落地影响有限。",[540],{"name":534,"url":531},[29,30,542,32,543],"农业扩张","生态系统评估",{"code":46,"slug":50,"name":545,"path":546,"parent_code":34},"生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity",[],{"status":549,"locked":14,"version":41,"method":42,"evidence":550},"classified",{"candidates":551,"agricultural_evidence":48},[552,557],{"code":46,"score":13,"supported":48,"title":553,"abstract":554,"tags":556},[],[29,555,50],"生态系统服务",[29],{"code":61,"score":62,"supported":48,"title":558,"abstract":559,"tags":560},[],[65,66],[30],[562,563],"巴西塞拉多 生态系统红色名录","塞拉多 遥感 生物多样性","巴西塞拉多生态系统红色名录-4821","10.1111\u002Fcsp2.70426",{"doi":565,"openalex_id":567,"authors":568,"venue":534,"cited_by_count":122,"oa_url":531,"card":575,"direction":387,"ingested_from":131},"W7221517508",[569,572],{"name":570,"orcid":571},"Juliana Ávila Martins","https:\u002F\u002Forcid.org\u002F0000-0002-5465-8276",{"name":573,"orcid":574},"Robert J. Smith","https:\u002F\u002Forcid.org\u002F0000-0003-1599-9171",{"tldr":576,"method":577,"finding":578,"direction":387,"opportunity":579},"用生态系统红色名录框架评估巴西塞拉多80个生态系统的崩溃风险。","基于1985-2020年遥感数据，按A2b、B1、B2标准评估分布与风险。","近四分之三生态系统受威胁，12个濒危、43个易危，湿地等空间受限类型突出。","可结合多源遥感与生态模型，动态监测农业扩张下生态系统风险并支撑保护修复决策。","2026-10-13T23:30:23.689100Z"]