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We systematically reviewed peer-reviewed English-language literature published between 2015 and 2025, focusing on Remote Sensing (RS)-driven agricultural drought monitoring and prediction. Eligible studies were identified from the Web of Science and Scopus databases (last searched in August 2025); review articles, duplicates, and irrelevant studies were excluded, resulting in 181 articles. This review critically examined the evolution of sensors, drought indices, modelling approaches, and emerging technologies used to assess agricultural drought across diverse agro-ecological regions. Specifically, we (1) systematically evaluated the use of optical, thermal, and microwave RS platforms, along with satellite-based, model-based, and reanalysis products for agricultural drought monitoring; (2) reviewed widely used RS-based drought indices, including single indices grouped into nine categories, as well as composite indices; (3) reviewed software or programming languages applied; (4) assessed the progression of modelling approaches from traditional statistical and time-series methods to machine learning (ML) and deep learning (DL) frameworks; and (5) examined validation strategies applied to agricultural drought assessment. The analysis reveals a growing research interest in this topic, with a strong geographic concentration in Asia, particularly China. Optical and thermal platforms (e.g. MODIS and Landsat) dominate the literature (~54%) due to their accessibility, followed by satellite-derived precipitation products (17%), while microwave soil-moisture missions remain underutilized (~7%). Regarding RS-based indices, most studies employed more than one index: 44% analysed multiple individual indices, 51% used composite indices (either alone or in combination with individual indices), and only 5% relied on a single index. Vegetation indices, such as NDVI, remain workhorses; however, their sensitivity to soil background effects and canopy saturation has driven increased adoption of thermal composites (e.g. VHI), as well as adaptive weighting schemes and principal component analysis-based approaches. Moreover, computational tools used in drought research can be broadly grouped into geospatial and RS platforms for large-scale data processing and analysis (e.g. QGIS, GEE), and general-purpose programming languages (e.g. R, Python) for model development and advanced analytics. Regarding the methodology, statistical and time-series approaches account for 64% of the reviewed studies, followed by ML models (20%) and DL methods (15%), while explainable artificial intelligence (XAI) remains nascent. Techniques, such as random forests, support vector machines, and CNN and LSTM architectures, generally outperform traditional models but require large training datasets and raise concerns related to interpretability and ethics. In terms of validation, hydro-climatic indices such as SPI and SPEI are most commonly used (32%), followed by crop yield and yield anomaly data (19%). This review further identifies key research gaps across sensors, indices, and modelling approaches and proposes a roadmap for developing equitable, interpretable, and resilient agricultural drought monitoring systems. Emerging opportunities include context-aware and interpretable AI, multi-sensor and multi-scale data fusion across optical, thermal, and microwave domains, integration of physiologically meaningful indicators such as sun-induced chlorophyll fluorescence (SIF), robust uncertainty quantification, and the development of hybrid and physics-informed modelling frameworks.","农业干旱是一种复杂的灾害，威胁粮食安全和可持续发展目标的实现。我们系统综述了2015年至2025年间发表的同行评议英文文献，聚焦于遥感（RS）驱动的农业干旱监测与预测。符合条件的研究通过Web of Science和Scopus数据库检索确定（最后检索时间为2025年8月）；综述文章、重复文献及不相关研究被排除，最终纳入181篇文献。本综述批判性地审视了用于评估不同农业生态区农业干旱的传感器、干旱指数、建模方法及新兴技术的演变。具体而言，我们（1）系统评估了光学、热红外和微波遥感平台，以及基于卫星、基于模型和再分析产品在农业干旱监测中的应用；（2）综述了广泛使用的基于遥感的干旱指数，包括归为九类的单一指数以及复合指数；（3）综述了所应用的软件或编程语言；（4）评估了建模方法从传统统计和时间序列方法向机器学习（ML）和深度学习（DL）框架的演进；（5）考察了应用于农业干旱评估的验证策略。分析显示，该主题的研究兴趣日益增长，地理上高度集中于亚洲，尤其是中国。光学和热红外平台（如MODIS和Landsat）因其可获取性在文献中占主导地位（约54%），其次是卫星降水产品（17%），而微波土壤水分任务仍未得到充分利用（约7%）。在基于遥感的指数方面，大多数研究采用了不止一种指数：44%分析了多个单一指数，51%使用了复合指数（单独使用或与单一指数结合使用），仅5%依赖单一指数。植被指数（如NDVI）仍是主力工具；然而，其对土壤背景效应和冠层饱和的敏感性推动了热红外复合指数（如VHI）以及自适应权重方案和基于主成分分析的方法得到更多采用。此外，干旱研究中使用的计算工具可大致分为用于大规模数据处理和分析的地理空间与遥感平台（如QGIS、GEE），以及用于模型开发的通用编程语言（如R、Python）。","GIScience & Remote Sensing","2026-10-08T00:00:00Z","论文",false,81,{"impact":361,"substance":321,"depth":584,"authority":345,"freshness":173,"relevant":455,"comment":585},19,"系统综述181篇文献，梳理遥感干旱监测的传感器、指数与建模进展，方法学价值高，但属学术综述，产业落地影响有限。",[587],{"name":578,"url":575},[589,590,591,592,593],"智慧农业","粮食安全","机器学习","遥感监测","农业干旱",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[596,597],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":605},[603,611,617,625,631,637,643,648],{"code":32,"score":604,"supported":605,"title":606,"abstract":608,"tags":610},15,true,[607],"remote sensing",[609,607],"遥感",[592],{"code":184,"score":345,"supported":605,"title":612,"abstract":614,"tags":616},[613],"drought",[615,613],"干旱",[],{"code":24,"score":385,"supported":605,"title":618,"abstract":619,"tags":624},[],[620,591,621,622,623],"深度学习","machine learning","deep learning","artificial intelligence",[591],{"code":263,"score":626,"supported":605,"title":627,"abstract":628,"tags":630},7,[],[590,629],"food security",[590],{"code":144,"score":632,"supported":605,"title":633,"abstract":634,"tags":636},6,[],[635,145],"土壤",[],{"code":388,"score":638,"supported":605,"title":639,"abstract":640,"tags":642},3,[],[641],"水产",[],{"code":176,"score":638,"supported":605,"title":644,"abstract":645,"tags":647},[],[646],"农业生态",[],{"code":16,"score":455,"supported":581,"title":649,"abstract":650,"tags":651},[],[],[589],[653,654],"遥感 农业干旱 监测","GIScience Remote Sensing 干旱指数","遥感农业干旱监测-4575","10.1080\u002F15481603.2026.2738981",{"doi":656,"openalex_id":658,"authors":659,"venue":578,"cited_by_count":424,"oa_url":669,"card":670,"direction":676,"ingested_from":677},"W7221081979",[660,663,666],{"name":661,"orcid":662},"Fatemeh Saba","https:\u002F\u002Forcid.org\u002F0000-0001-6171-155X",{"name":664,"orcid":665},"Nima Ahmadian","https:\u002F\u002Forcid.org\u002F0000-0003-3676-1840",{"name":667,"orcid":668},"Olena Dubovyk","https:\u002F\u002Forcid.org\u002F0000-0002-7338-3167","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F15481603.2026.2738981?needAccess=true",{"tldr":671,"method":672,"finding":673,"direction":674,"opportunity":675},"系统综述2015-2025年遥感农业干旱监测与预测的传感器、指数、建模及验证进展。","基于Web of Science与Scopus的181篇文献系统综述，涵盖光学\u002F","光学热红外平台占主导，微波土壤水分利用不足；复合指数与机器学习建模成为趋势。","农业遥感与作物表型","微波土壤水分数据融合与深度学习干旱预测模型在数据稀缺区验证是潜在研究空白。","智慧农业 \u002F 农业物联网","openalex","2026-10-10T23:30:14.933078Z",{"id":680,"title":681,"url":682,"summary":683,"summary_zh":684,"content":11,"source_name":685,"source_url":682,"published_at":686,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":687,"score_detail":688,"sources":691,"tags":693,"primary_topic":698,"secondary_topics":699,"classification":700,"search_phrases":728,"slug":731,"view_count":455,"doi":732,"paper":733,"created_at":750},4563,"Seeing Crops in 3D: Remote Sensing for Smarter Precision Farming","https:\u002F\u002Fdoi.org\u002F10.12688\u002Fopenreseurope.25171.1","The growing adoption of digital technologies in agriculture is opening new opportunities for improving productivity, sustainability and resource efficiency through data-driven decision making. This paper presents the integrated precision-agriculture framework developed by RFSAT Limited within the H2020 AgriBIT project and currently being further enhanced through the ENACT project. The framework combines Internet-of-Things (IoT) sensing, centimetre-accuracy Global Navigation Satellite System (GNSS) positioning, high-resolution multispectral remote sensing and Artificial Intelligence (AI) analytics into a single smart-farming solution. A key enabler is a low-cost, centimetre-accurate GNSS receiver developed by RFSAT, which allows precise georeferencing of aerial and ground observations and, in turn, the generation of high-resolution multispectral 3D models of crop canopies with spatial resolution exceeding 1 cm\u002Fpixel. These digital representations support detailed monitoring of crop growth, canopy development, biomass distribution and plant health throughout the season. Fused with an IoT monitoring infrastructure of environmental and microclimatic sensors, the georeferenced remote-sensing products form a comprehensive digital twin of the agricultural environment, over which machine-learning techniques automatically identify and localise crop anomalies including water stress, nutrient deficiencies, disease outbreaks and pest infestations. The technologies have been validated at operational pilot sites across Greece, Portugal and Italy, spanning orchards, vineyards and other high-value crops, where the combination of centimetre positioning, multispectral 3D modelling, IoT monitoring and AI analytics substantially improved monitoring accuracy, reduced input consumption and enhanced management decisions.","数字技术在农业中的日益普及，正通过数据驱动的决策为提升生产力、可持续性和资源效率开辟新的机遇。本文介绍了RFSAT有限公司在H2020 AgriBIT项目中开发的综合精准农业框架，该框架目前正通过ENACT项目进一步完善。该框架将物联网（IoT）传感、厘米级精度的全球导航卫星系统（GNSS）定位、高分辨率多光谱遥感以及人工智能（AI）分析整合为一个智慧农业解决方案。一个关键的使能因素是由RFSAT开发的低成本、厘米级精度GNSS接收器，它能够对航空和地面观测进行精确的地理参照，进而生成空间分辨率超过1厘米\u002F像素的作物冠层高分辨率多光谱三维模型。这些数字表示支持在整个生长季对作物生长、冠层发育、生物量分布和植物健康进行详细监测。通过与由环境和小气候传感器组成的物联网监测基础设施相融合，地理参照的遥感产品形成了农业环境的综合数字孪生，机器学习技术在此基础上自动识别和定位作物异常，包括水分胁迫、养分缺乏、病害暴发和虫害侵扰。这些技术已在希腊、葡萄牙和意大利的运营试点基地进行了验证，涵盖果园、葡萄园和其他高价值作物，其中厘米级定位、多光谱三维建模、物联网监测和AI分析的结合大幅提高了监测精度、降低了投入品消耗并改善了管理决策。","Open Research Europe","2026-10-09T00:00:00Z",80,{"impact":361,"substance":321,"depth":584,"authority":689,"freshness":173,"relevant":455,"comment":690},13,"欧盟H2020项目论文，融合厘米级GNSS、多光谱3D建模与AI分析，方法新颖且多国验证，对智慧农业技术路线有参考价值。",[692],{"name":685,"url":682},[589,694,695,696,697],"农业人工智能","农业遥感","数字孪生","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":701},{"candidates":702,"agricultural_evidence":605},[703,708,713,717,723],{"code":32,"score":361,"supported":605,"title":704,"abstract":705,"tags":707},[607],[609,607,706],"multispectral",[695],{"code":16,"score":416,"supported":605,"title":709,"abstract":710,"tags":712},[],[589,697,696,711],"digital twin",[696,589,697],{"code":24,"score":626,"supported":605,"title":714,"abstract":715,"tags":716},[],[591,623],[694],{"code":48,"score":632,"supported":605,"title":718,"abstract":719,"tags":722},[],[720,721],"物联网","IoT",[],{"code":286,"score":638,"supported":605,"title":724,"abstract":725,"tags":727},[],[726],"葡萄",[],[729,730],"AgriBIT 精准农业 遥感","GNSS 多光谱 3D 作物","AgriBIT精准农业遥感-4563","10.12688\u002Fopenreseurope.25171.1",{"doi":732,"openalex_id":734,"authors":735,"venue":685,"cited_by_count":424,"oa_url":682,"card":745,"direction":676,"ingested_from":677},"W7221178461",[736,739,742],{"name":737,"orcid":738},"Artur Krukowski","https:\u002F\u002Forcid.org\u002F0000-0002-6635-7585",{"name":740,"orcid":741},"Emmanouela Vogiatzaki","https:\u002F\u002Forcid.org\u002F0000-0001-9239-2045",{"name":743,"orcid":744},"İzzet Kale","https:\u002F\u002Forcid.org\u002F0000-0001-5562-6885",{"tldr":746,"method":747,"finding":748,"direction":674,"opportunity":749},"提出融合物联网、厘米级GNSS、多光谱遥感和AI的精准农业框架，构建作物数字孪生。","低成本厘米级GNSS、多光谱3D建模、IoT传感器与机器学习，在希腊、葡萄牙、意","厘米定位与多光谱3D建模结合AI可提升监测精度、减少投入并改善管理决策。","可探索低成本厘米级GNSS与多光谱3D数字孪生在更多作物和区域的可扩展性及实时决策优化。","2026-10-10T23:30:07.913557Z",{"id":752,"title":753,"url":754,"summary":755,"summary_zh":756,"content":11,"source_name":757,"source_url":754,"published_at":758,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":759,"sources":761,"tags":763,"primary_topic":767,"secondary_topics":768,"classification":769,"search_phrases":780,"slug":783,"view_count":455,"doi":784,"paper":785,"created_at":804},4513,"Spatio-temporal assessment of mining-induced environmental changes using remote sensing: a case study of the Talcher Coalfield, Angul District, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74815-7","This study evaluates the spatio-temporal impacts of coal mining on environmental conditions in the Talcher coalfield region, India, using multi-temporal Landsat data (2000, 2014, and 2023) at 30 m resolution. Land use land cover (LULC) was derived using supervised classification (Maximum Likelihood Algorithm), while vegetation dynamics and thermal characteristics were assessed using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). In addition, atmospheric column densities were incorporated to analyze key atmospheric pollutants SO₂, NO₂, and CO and to assess air quality variations associated with mining activities. The integrated assessment highlights substantial environmental changes associated with mining and land-use transformation. The LULC analysis (2000–2023) reveals a decline in forest cover (41.33% to 37.37%) and agricultural land (45.62% to 45.04%), accompanied by an increase in built-up areas (6.84% to 8.54%) and mining areas (2.12% to 4.94%), indicating intensified anthropogenic pressure. LST and NDVI analyses (2014–2023) show changes in surface thermal conditions and vegetation characteristics. LST results indicate higher maximum and mean temperatures in mining and built-up areas, with changes between 2014 and 2023 varying among land-use classes, with mining areas showing the highest thermal values, where mean summer LST increased from 42.97 °C (2014) to 46.28 °C (2023), and mean winter LST increased from 38.67 °C to 41.81 °C. NDVI analysis indicates a decline in maximum NDVI values over the study period. The results show a spatial association between LULC changes, NDVI, and LST, with areas of mining and built-up expansion generally corresponding to lower NDVI and higher surface temperatures. Analysis of satellite-derived atmospheric column densities indicates higher SO₂, NO₂, and CO values in mining and industrial regions, with higher values observed during winter.","本研究利用30 m分辨率的多时相Landsat数据（2000年、2014年和2023年），评估了印度塔尔切尔煤田地区煤矿开采对环境条件的时空影响。土地利用\u002F土地覆盖（LULC）通过监督分类（最大似然算法）获得，植被动态和热特征则分别采用归一化差异植被指数（NDVI）和地表温度（LST）进行评估。此外，研究还引入大气柱密度以分析主要大气污染物SO₂、NO₂和CO，并评估与采矿活动相关的空气质量变化。综合评估揭示了与采矿和土地利用转型相关的显著环境变化。LULC分析（2000—2023年）显示，森林覆盖面积（从41.33%降至37.37%）和农业用地（从45.62%降至45.04%）减少，而建成区面积（从6.84%增至8.54%）和采矿区域面积（从2.12%增至4.94%）增加，表明人为压力加剧。LST和NDVI分析（2014—2023年）显示地表热条件和植被特征发生变化。LST结果表明，采矿区和建成区的最高温度和平均温度较高，2014年至2023年的变化因土地利用类型而异，其中采矿区的热值最高，夏季平均LST从42.97 °C（2014年）升至46.28 °C（2023年），冬季平均LST从38.67 °C升至41.81 °C。NDVI分析表明，研究期内最大NDVI值下降。结果表明，LULC变化、NDVI和LST之间存在空间关联，采矿和建成区扩张区域通常对应较低的NDVI和较高的地表温度。卫星反演的大气柱密度分析表明，采矿和工业区域的SO₂、NO₂和CO值较高，且冬季观测值更高。","Scientific Reports","2026-10-07T00:00:00Z",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":760},"该研究聚焦印度煤矿区环境遥感监测，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。",[762],{"name":757,"url":754},[592,764,765,766],"地表温度","矿区生态","土地覆盖变化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":770},{"candidates":771,"agricultural_evidence":605},[772,776],{"code":32,"score":385,"supported":605,"title":773,"abstract":774,"tags":775},[607],[],[764,592],{"code":176,"score":69,"supported":581,"title":777,"abstract":778,"tags":779},[],[],[766,765],[781,782],"Talcher 煤田 遥感","Landsat NDVI LST 矿区","Talcher煤田遥感-4513","10.1038\u002Fs41598-026-74815-7",{"doi":784,"openalex_id":786,"authors":787,"venue":757,"cited_by_count":424,"oa_url":798,"card":799,"direction":674,"ingested_from":677},"W7220855962",[788,790,792,795],{"name":789,"orcid":11},"Sk Riyajul Islam",{"name":791,"orcid":11},"Shilpa Pakhira",{"name":793,"orcid":794},"Abhilash Gogineni","https:\u002F\u002Forcid.org\u002F0000-0002-8350-468X",{"name":796,"orcid":797},"Regasa Yadeta Sembeta","https:\u002F\u002Forcid.org\u002F0000-0002-9686-3155","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74815-7_reference.pdf",{"tldr":800,"method":801,"finding":802,"direction":674,"opportunity":803},"基于多时相Landsat遥感评估印度煤矿区2000-2023年环境变化。","Landsat多时相数据、最大似然监督分类、NDVI、LST及SO₂\u002FNO₂\u002FC","采矿与建设用地扩张导致林地耕地减少，矿区夏季均温升至46.28°C，NDVI下降，污染物冬季偏高。","可借鉴其多指标遥感框架，研究矿区周边农田退化与作物表型响应，构建农业环境风险监测模型。","2026-10-09T23:30:27.643699Z",{"id":806,"title":807,"url":808,"summary":809,"summary_zh":810,"content":11,"source_name":811,"source_url":808,"published_at":758,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":812,"sources":814,"tags":816,"primary_topic":817,"secondary_topics":818,"classification":819,"search_phrases":831,"slug":832,"view_count":424,"doi":833,"paper":834,"created_at":855},4512,"Analysis of urban heat islands in the study of urban environmental problems based on remote sensing","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fgac.2026.24317","As global urbanization accelerates cities increasingly face ecological challenges, most notably the Urban Heat Island (UHI) effect. This study investigates the formation and spatial distribution of UHI’s in the cities of Mingachevir, Shirvan and Yevlakh, located in the central region of the Republic of Azerbaijan. Utilizing ArcGIS, the analysis processed Landsat 8 OLI\u002FTIRS satellite imagery from August 2023. The analysis revealed that, despite their relatively small size, distinct urban heat islands have formed in the studied cities. The results demonstrate the effectiveness of satellite-based methods for identifying urban heat islands and can be used to support urban planning, environmental monitoring, and climate adaptation strategies in the region. The results indicate a strong correlation between urban urban density and thermal concentration, with LST values in urban cores reaching up to 42 °C, which is 2 °C higher than the surrounding rural landscapes. Analysis reveals that industrial zones and areas with low albedo surfaces (asphalt and concreate) are primary drivers of thermal intensity in these cities. To mitigate these effects, the study recommends the implementation of “cool roofs”, the expansion of urban green corridors, and the integration of permeable pavements into local zoning laws. These findings provide a quantiative framework for urban planners and environmental policymakers in Azerbaijan to develop targeted climate adaptation strategies and improve urban thermal comfort.","随着全球城市化进程加快，城市日益面临生态挑战，其中最突出的是城市热岛（Urban Heat Island, UHI）效应。本研究探讨了位于阿塞拜疆共和国中部地区的明盖恰乌尔、希尔万和叶夫拉赫等城市热岛的形成及其空间分布。研究利用ArcGIS对2023年8月的Landsat 8 OLI\u002FTIRS卫星影像进行了处理分析。分析表明，尽管这些城市规模相对较小，但已形成明显的城市热岛。结果表明，基于卫星的方法在识别城市热岛方面具有有效性，可用于支持该地区的城市规划、环境监测和气候适应策略。研究结果显示，城市密度与热量集中之间存在很强的相关性，城市核心区的地表温度（LST）值最高可达42 °C，比周边乡村景观高出2 °C。分析表明，工业区和低反照率地表区域（沥青和混凝土）是这些城市热强度的主要驱动因素。为缓解这些影响，研究建议实施“冷屋顶”、扩展城市绿色廊道，并将透水铺装纳入地方分区法规。这些发现为阿塞拜疆的城市规划者和环境政策制定者提供了一个定量框架，以制定有针对性的气候适应策略并改善城市热舒适度。","Geodesy and Cartography",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":813},"研究阿塞拜疆城市热岛效应，属城市环境遥感，与三农、农业信息化、智慧农业无关。",[815],{"name":811,"url":808},[580],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":820},{"candidates":821,"agricultural_evidence":605},[822,826],{"code":32,"score":173,"supported":605,"title":823,"abstract":824,"tags":825},[607],[],[],{"code":184,"score":638,"supported":605,"title":827,"abstract":828,"tags":830},[],[829],"气候适应",[],[],"Analysisofurbanheatislandsinthestudyofur-4512","10.3846\u002Fgac.2026.24317",{"doi":833,"openalex_id":835,"authors":836,"venue":811,"cited_by_count":424,"oa_url":849,"card":850,"direction":674,"ingested_from":677},"W7220845208",[837,840,843,846],{"name":838,"orcid":839},"Shahnaz Amanova","https:\u002F\u002Forcid.org\u002F0000-0001-7962-7073",{"name":841,"orcid":842},"Afag Hajiyeva","https:\u002F\u002Forcid.org\u002F0000-0002-9813-7835",{"name":844,"orcid":845},"Leyla P. Ibrahimova","https:\u002F\u002Forcid.org\u002F0009-0005-3955-1074",{"name":847,"orcid":848},"Firuza M. Jafarova","https:\u002F\u002Forcid.org\u002F0000-0003-1342-1843","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F24317\u002F13520",{"tldr":851,"method":852,"finding":853,"direction":674,"opportunity":854},"基于Landsat 8遥感影像分析阿塞拜疆三座城市的热岛效应及其空间分布。","使用ArcGIS处理2023年8月Landsat 8 OLI\u002FTIRS影像，反演","城市核心LST达42°C，比郊区高2°C，工业区和低反照率地表是主要驱动因素。","可结合多时相遥感与地表覆盖数据，研究小城市热岛动态及其与城市扩张的耦合机制。","2026-10-09T23:30:27.537653Z",{"id":857,"title":858,"url":859,"summary":860,"summary_zh":861,"content":11,"source_name":862,"source_url":859,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":863,"score_detail":864,"sources":868,"tags":870,"primary_topic":873,"secondary_topics":874,"classification":876,"search_phrases":903,"slug":906,"view_count":424,"doi":907,"paper":908,"created_at":929},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)",68,{"impact":416,"substance":361,"depth":865,"authority":689,"freshness":866,"relevant":455,"comment":867},16,9,"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[869],{"name":862,"url":859},[589,871,694,609,872],"无人机","葡萄园管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[875],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":877},{"candidates":878,"agricultural_evidence":605},[879,883,887,892,897],{"code":32,"score":361,"supported":605,"title":880,"abstract":881,"tags":882},[706],[609,607,706],[609],{"code":24,"score":604,"supported":605,"title":884,"abstract":885,"tags":886},[622],[620,622],[694],{"code":16,"score":626,"supported":605,"title":888,"abstract":889,"tags":891},[],[697,890],"precision agriculture",[589],{"code":286,"score":632,"supported":605,"title":893,"abstract":894,"tags":896},[],[726,895],"grape",[],{"code":40,"score":632,"supported":605,"title":898,"abstract":899,"tags":902},[],[900,901],"空间分析","GIS",[],[904,905],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":907,"openalex_id":909,"authors":910,"venue":862,"cited_by_count":424,"oa_url":859,"card":924,"direction":674,"ingested_from":677},"W7220993218",[911,913,916,918,921],{"name":912,"orcid":11},"Dimitra Passa",{"name":914,"orcid":915},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":917,"orcid":11},"Georgios Nektarios Tselos",{"name":919,"orcid":920},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":922,"orcid":923},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":925,"method":926,"finding":927,"direction":674,"opportunity":928},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":931,"title":932,"url":933,"summary":934,"summary_zh":935,"content":11,"source_name":936,"source_url":933,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":937,"score_detail":938,"sources":940,"tags":942,"primary_topic":946,"secondary_topics":947,"classification":950,"search_phrases":975,"slug":978,"view_count":455,"doi":979,"paper":980,"created_at":994},4504,"Development of the Potential Evapotranspiration-Vegetation-Precipitation Drought Index (PETVPDI) and Its Application in West Africa Using Remote Sensing Datasets","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193435","Drought remains one of the most severe climate-related hazards affecting water resources, ecosystems, and agricultural productivity in West Africa (WA). Traditional drought indices often rely on individual hydroclimatic or vegetation variables and may not fully represent the coupled effects of precipitation supply, atmospheric evaporative demand, and vegetation response. To address this limitation, this study develops and applies a composite drought indicator, the Potential Evapotranspiration–Vegetation–Precipitation Drought Index (PETVPDI), using multi-source remote sensing datasets over WA for 2001–2022. PETVPDI integrates precipitation (P), potential evapotranspiration (PET), and the normalized difference vegetation index (NDVI) within a three-dimensional Euclidean-distance framework. The index was evaluated against GLDAS soil moisture (SM), ESA CCI SM, gross primary productivity (GPP), and net primary productivity (NPP), and compared with four conventional remote-sensing drought indices (cRSDI): VCI, VHI, TVDI, and TVPDI. Deseasonalized anomaly correlations, used as the primary validation evidence to reduce the influence of the shared monsoon seasonal cycle, show that PETVPDI achieved the highest numerical correlations among the evaluated indices with GLDAS SM (R = 0.556, p \u003C 0.001), ESA CCI SM (R = 0.513, p \u003C 0.001), and GPP (R = 0.483, p \u003C 0.001). Steiger’s tests for dependent overlapping correlations show that the PETVPDI–TVPDI difference was statistically significant for raw GLDAS SM and deseasonalized ESA CCI SM, but not for the other evaluated comparisons. Spatial PETVPDI patterns represent each pixel’s relative position within its own 2001–2022 hydroclimatic and vegetation range and therefore should not be interpreted as direct differences in absolute climatic aridity among locations. Long-term trends are spatially heterogeneous across WA. Overall, PETVPDI provides an integrated framework for monitoring relative drought–wetness variability and offers complementary information for drought assessment and early-warning applications in data-scarce regions.","干旱仍然是影响西非（WA）水资源、生态系统和农业生产力最严重的气候相关灾害之一。传统干旱指数通常依赖单一的水文气候或植被变量，可能无法充分反映降水供给、大气蒸发需求和植被响应之间的耦合效应。为解决这一局限，本研究利用2001—2022年西非多源遥感数据集，开发并应用了一种复合干旱指标——潜在蒸散–植被–降水干旱指数（PETVPDI）。PETVPDI在三维欧氏距离框架内整合了降水（P）、潜在蒸散（PET）和归一化差异植被指数（NDVI）。该指数以GLDAS土壤湿度（SM）、ESA CCI SM、总初级生产力（GPP）和净初级生产力（NPP）进行评估，并与四种传统遥感干旱指数（cRSDI）：VCI、VHI、TVDI和TVPDI进行比较。以去季节化异常相关性作为主要验证依据，以减少共同季风季节循环的影响，结果表明PETVPDI在所评估的指数中与GLDAS SM（R = 0.556，p \u003C 0.001）、ESA CCI SM（R = 0.513，p \u003C 0.001）和GPP（R = 0.483，p \u003C 0.001）的数值相关性最高。针对相依重叠相关性的Steiger检验表明，PETVPDI–TVPDI差异在原始GLDAS SM和去季节化ESA CCI SM上具有统计学显著性，但在其他评估比较中不显著。PETVPDI的空间格局表示每个像元在其自身2001—2022年水文气候和植被范围内的相对位置，因此不应被解释为地点之间绝对气候干燥度的直接差异。长期趋势在西非呈现空间异质性。总体而言，PETVPDI为监测相对干旱–湿润变率提供了一个集成框架，并为数据稀缺地区的干旱评估和预警应用提供了补充信息。","Remote Sensing",75,{"impact":416,"substance":321,"depth":361,"authority":345,"freshness":866,"relevant":455,"comment":939},"提出融合降水、蒸散与植被的复合干旱指数，方法新颖、验证扎实，对数据稀缺区农业干旱预警有参考价值，但区域局限于西非，国内应用关联度有限。",[941],{"name":936,"url":933},[590,943,592,944,945],"农业气象","西非农业","干旱指数",{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},[948,949],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":951},{"candidates":952,"agricultural_evidence":605},[953,957,961,967,971],{"code":184,"score":865,"supported":605,"title":954,"abstract":955,"tags":956},[613],[615,613],[943,945],{"code":32,"score":604,"supported":605,"title":958,"abstract":959,"tags":960},[607],[609,607],[592],{"code":152,"score":345,"supported":605,"title":962,"abstract":964,"tags":966},[963],"evapotranspiration",[965,963],"水资源",[],{"code":144,"score":632,"supported":605,"title":968,"abstract":969,"tags":970},[],[635,145],[],{"code":263,"score":455,"supported":581,"title":972,"abstract":973,"tags":974},[],[],[590],[976,977],"PETVPDI 干旱指数 西非","遥感 干旱监测 NDVI","PETVPDI干旱指数西非-4504","10.3390\u002Frs18193435",{"doi":979,"openalex_id":981,"authors":982,"venue":936,"cited_by_count":424,"oa_url":933,"card":989,"direction":674,"ingested_from":677},"W7220963372",[983,986],{"name":984,"orcid":985},"Abdoul-Aziz Bio Sidi D. Bouko","https:\u002F\u002Forcid.org\u002F0009-0003-1179-8046",{"name":987,"orcid":988},"Bing Gao","https:\u002F\u002Forcid.org\u002F0000-0003-0782-8858",{"tldr":990,"method":991,"finding":992,"direction":674,"opportunity":993},"构建融合蒸散、植被与降水的复合干旱指数PETVPDI，并用于西非2001—2022年干旱监测。","多源遥感数据，三维欧氏距离整合P、PET、NDVI，与土壤湿度、GPP\u002FNPP及","PETVPDI与土壤湿度和GPP的相关性最高，能更好反映干旱-湿润相对变化，但趋势空间异质。","可将该复合指数迁移至其他数据稀缺区，并结合作物产量或灌溉需水做农业干旱预警与适应性研究。","2026-10-09T23:30:25.838340Z",{"id":996,"title":997,"url":998,"summary":999,"summary_zh":11,"content":11,"source_name":1000,"source_url":998,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1001,"score_detail":1002,"sources":1004,"tags":1006,"primary_topic":1008,"secondary_topics":1009,"classification":1012,"search_phrases":1031,"slug":1034,"view_count":455,"doi":1035,"paper":1036,"created_at":1068},4500,"Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11163942\u002Fv1","Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square","Research Square",74,{"impact":361,"substance":508,"depth":361,"authority":385,"freshness":173,"relevant":455,"comment":1003},"将无人机遥感与深度学习结合用于水稻白叶枯病早期检测，方法有创新、结论具应用价值，但属预印本且为细分技术进展，适合进入主题聚合而非头条精选。",[1005],{"name":1000,"url":998},[589,694,210,609,1007],"病害检测",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1010,1011],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1013},{"candidates":1014,"agricultural_evidence":605},[1015,1019,1023,1027],{"code":208,"score":416,"supported":605,"title":1016,"abstract":1017,"tags":1018},[209],[209],[210],{"code":24,"score":416,"supported":605,"title":1020,"abstract":1021,"tags":1022},[622],[622],[694],{"code":32,"score":416,"supported":605,"title":1024,"abstract":1025,"tags":1026},[607],[607],[609],{"code":16,"score":455,"supported":581,"title":1028,"abstract":1029,"tags":1030},[],[],[589],[1032,1033],"无人机遥感 水稻 白叶枯病","深度学习 水稻病害 早期检测","无人机遥感水稻白叶枯病-4500","10.21203\u002Frs.3.rs-11163942\u002Fv1",{"doi":1035,"openalex_id":1037,"authors":1038,"venue":1000,"cited_by_count":424,"oa_url":998,"card":11,"direction":674,"ingested_from":677},"W7221055917",[1039,1042,1045,1047,1049,1052,1054,1056,1059,1061,1063,1065],{"name":1040,"orcid":1041},"Carlito B. Balingbing","https:\u002F\u002Forcid.org\u002F0000-0002-3672-9253",{"name":1043,"orcid":1044},"Dmytro Chebotarov","https:\u002F\u002Forcid.org\u002F0000-0003-1351-9453",{"name":1046,"orcid":11},"Ian  Carlo Perilla",{"name":1048,"orcid":11},"Stephen Klassen",{"name":1050,"orcid":1051},"Nancy Castilla","https:\u002F\u002Forcid.org\u002F0000-0002-9143-4933",{"name":1053,"orcid":11},"Raphael  Gabriel Carvajal",{"name":1055,"orcid":11},"Kendrix Ortega",{"name":1057,"orcid":1058},"Joseph M. Sandro","https:\u002F\u002Forcid.org\u002F0000-0002-8311-2299",{"name":1060,"orcid":11},"Nurmi Pangesti",{"name":1062,"orcid":11},"Caesar Arloo R. Centeno",{"name":1064,"orcid":11},"Arnel Rala",{"name":1066,"orcid":1067},"Li-Yu  Daisy Liu","https:\u002F\u002Forcid.org\u002F0000-0001-6997-8101","2026-10-09T23:30:18.390549Z",{"id":1070,"title":1071,"url":1072,"summary":1073,"summary_zh":1074,"content":11,"source_name":757,"source_url":1072,"published_at":758,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":1075,"sources":1077,"tags":1079,"primary_topic":1082,"secondary_topics":1083,"classification":1084,"search_phrases":1103,"slug":1106,"view_count":69,"doi":1107,"paper":1108,"created_at":1124},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":866,"relevant":455,"comment":1076},"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[1078],{"name":757,"url":1072},[589,694,609,1080,1081],"NDVI","土地利用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1085},{"candidates":1086,"agricultural_evidence":605},[1087,1091,1095,1099],{"code":32,"score":689,"supported":605,"title":1088,"abstract":1089,"tags":1090},[706],[706],[1080,609],{"code":24,"score":626,"supported":605,"title":1092,"abstract":1093,"tags":1094},[],[620,622],[694],{"code":176,"score":455,"supported":581,"title":1096,"abstract":1097,"tags":1098},[],[],[1081],{"code":16,"score":455,"supported":581,"title":1100,"abstract":1101,"tags":1102},[],[],[589],[1104,1105],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497","10.1038\u002Fs41598-026-67985-x",{"doi":1107,"openalex_id":1109,"authors":1110,"venue":757,"cited_by_count":424,"oa_url":1118,"card":1119,"direction":676,"ingested_from":677},"W7220869344",[1111,1113,1115],{"name":1112,"orcid":11},"Neeraj Kumar",{"name":1114,"orcid":11},"Navin Rajpal",{"name":1116,"orcid":1117},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":1120,"method":1121,"finding":1122,"direction":674,"opportunity":1123},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","2026-10-09T23:30:12.234854Z",{"id":1126,"title":1127,"url":1128,"summary":1129,"summary_zh":11,"content":1130,"source_name":1131,"source_url":11,"published_at":1132,"category":1133,"cover_url":11,"hotness":385,"is_selected":581,"score":1134,"score_detail":1135,"sources":1139,"tags":1141,"primary_topic":1145,"secondary_topics":1146,"classification":1148,"search_phrases":1170,"slug":1173,"view_count":408,"doi":11,"paper":11,"created_at":1174},4464,"山东省农科院发布舜耕粮安农业遥感大模型 精度95%效率提升超100%","http:\u002F\u002Fwww.md2we.com\u002Fitems\u002F4263","山东省农科院发布舜耕粮安农业遥感大模型，基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产全链条智能监测体系，精度高达95%，效率提升超100%；山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。","每日热点 大众新闻 2026-10-04·10月4日 · 周日 报道\n\n阅读 0\n\nAI 评分\n\n精选\n\n影响力 24\n\n信息增量 22\n\n专业深度 18\n\n信源权威 10\n\n时效性 8\n\n省级“人工智能+农业”行动部署叠加舜耕·粮安遥感大模型95%精度等实质数据，信息增量与专业深度俱佳，值得进入每日精选。\n\n摘要\n\n山东省农科院发布舜耕粮安农业遥感大模型，基于20年2000万份人工样本，覆盖山东全域全地貌全物候周期，构建耕地智能提取-肥力墒情预测-作物精准识别-长势动态监测-病虫草害预警-作物智能估产全链条智能监测体系，精度95%、效率提升超100%。山东启动40个实数融合数字农业发展县建设，培育智慧农业应用场景近1000个。\n\n文 全文\n\n### 山东实施“人工智能+农业”行动：数据“种”进泥土，云端守护丰收\n\n2026-10-06 02:39:49\n\n![Image 1: Image 1](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a29336dd947_750.JPEG)\n\n麦田上空，无人机低空掠过，哪片麦苗缺氮、哪处有蚜虫，屏幕上即时标红；大棚内，人工智能（AI）算法值守，缺水、缺肥时，系统立刻报警；分拣车间里，AI视觉系统每秒“端详”上百颗苹果，按色泽、果径、瑕疵精准分级，误判率不到百分之一……\n\n从“靠天吃饭”到“知天而作”，当前人工智能正成为助力农业稳产提质、带动农民增收致富的“好帮手”，为农业农村现代化提供全新范式。\n\n2026年中央一号文件明确提出“因地制宜发展农业新质生产力，促进人工智能与农业发展相结合”，将AI技术从单点应用升级为贯穿农业全产业链的战略引擎。多家行业机构预测，2026年中国智慧农业市场规模将突破1340亿元，年复合增长率保持在15%以上。\n\n刚刚召开的省委十二届十一次全会，着眼把农业建成现代化大产业，推动农业数智化转型升级，明确提出实施“人工智能+农业”行动。何谓“人工智能+农业”？简言之，就是以人工智能技术为核心，驱动传统农业向智能化、高效化转型的新型生产体系。与会同志认为，从更深层次看，“人工智能+农业”不是简单的技术叠加，而是农业生产方式、组织形式和价值链条的系统性重塑。\n\n**AI种地，田野阡陌释放出更多可能**\n\n技术的生命力在于解决实际问题。放眼全国，农业面临着劳动力老龄化、气候变化风险加剧等压力，也存在特色产业附加值低、品牌力不强等问题。人工智能的介入推动农业从“靠经验”转向“靠数据”，为端牢“中国饭碗”、塑强农业产业注入确定性。在不少人看来，人工智能的应用推广，恰逢其时。\n\n“山东是农业大省，也是农业产业化发源地，产业规模大、业态全，生产者队伍素质高、接受新事物能力强。与此同时，山东工业基础雄厚，人工智能技术发展迅速。新技术在农业方面的转化推广具有显著的成本和效率优势。”省委党校（山东行政学院）教授、山东乡村振兴研究院院长刘岳认为，山东有着发展“人工智能+农业”的坚实基础，在完整的产业体系、可观市场规模、丰富应用场景等优势加持下，田野阡陌将释放出更多可能。\n\n夏日的鲁北平原，玉米拔节生长，一片生机盎然。在齐河县金穗粮食种植专业合作社的示范田里，一套黑色管网发挥着大作用。“这是我们铺设的水肥一体化设施。每30厘米有一个出水孔，能精准滴灌到玉米根底部位置。相比大水漫灌，它的灌溉速度快3倍，可省水30%，一人一天能管理200亩地。”合作社理事长袁本刚说。依托大数据平台，合作社还可在“云端”实施全程监管，动动手指就能依据检测数据制订分地块水肥管理方案，为各村玉米田定制个性化基肥配方，同步开展测土配方、作物叶绿素检测等服务。\n\n今年山东夏粮逆势丰收，连续4年实现面积、单产、总产“三增”，这离不开水肥一体化技术在大田的推广。数据显示，2025年，全省应用水肥一体化技术的粮油作物面积已达到630万亩。“水肥一体化与减垄增地、‘大小行’密植等结合，加速了良种、良法、良机、良田深度融合，有效提升了农田规模化、标准化种植水平。”袁本刚体会深切，水肥一体化的落地，让丰收有了更充实的底气。\n\n![Image 2: Image 2](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a30f6bab0_750.JPEG)\n\n种子是农业的“芯片”，也是保障粮食安全的关键，智能育种已成为育种科研人员聚焦的热点领域。\n\n今年4月，由中国农业科学院作物科学研究所与山东极智生物科技有限公司联合研发的“iWheat小麦智能设计育种平台”发布。对青岛农业大学小麦遗传育种团队来说，iWheat的应用正在帮助其攻克小麦育种“周期长、效率低、成本高”等行业难题。\n\n这一平台整合3万余份小麦种质资源的基因型、442个功能基因、6000余个功能位点及40余万条表型数据，为每一份种质建立起“数字身份证”，实现资源的精准检索与高效利用。“结合快速繁育体系，能将传统8年的育种周期压缩2至3年。”团队负责人张玉梅说，加速新品种的培育进程、降低育种成本，意味着更能提升整个种业行业的创新能力和竞争力，进而赋能小麦产业链上下游实现高质量发展。\n\n**让“技术盆景”真正落地为“产业风景”**\n\n农业大模型能够整合气象、土壤、作物生长等多维度数据，显著提升农业生产的精准性和预见性。我省明确，要加强农业大模型与人工智能体落地应用。\n\n在“中国蔬菜之乡”寿光，蔬菜大棚迎来了一批特殊“员工”。一群脚步灵活的机器狗，沿着田垄灵活穿梭，搭载的高清摄像头与传感器如同“火眼金睛”，精准识别病虫害迹象。巡检结束，数据实时传回后台，人工智能模型迅速给出种植决策。如今，寿光新建大棚已迭代升级至第七代智能物联网“云棚”，物联网应用率超过85%，劳动生产效率较以往翻倍。\n\n![Image 3: Image 3](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fappimg.dzwww.com%2Fshare%2F2026%2F08%2F02%2Ff9c9a2a2155c1ff_750.JPEG)\n\n就在一个多月前，省农科院发布“舜耕·粮安”农业遥感大模型。省农科院党委书记梁金光介绍，该模型基于20年来积累的2000万份人工样本的海量数据，覆盖山东全域全地貌、全物候周期，构建起“耕地智能提取—肥力墒情预测—作物精准识别—长势动态监测—病虫草害预警—作物智能估产”全链条智能监测体系，精度高达95%，效率提升超100%。\n\n在中游加工环节，大模型也能有效提升产品附加值与标准化水平。在烟台莱山区，由浪潮云洲旗下数农云（山东）信息科技有限公司联合中国农业大学计算中心、烟台葡乡农业开发有限公司共同打造的“莱山葡萄种植和酿造大模型”，一方面在种植端自动生成气候适应性调控方案，实现水肥精准配施、病虫害智能预警与防治，葡萄产量与品质同步提升；另一方面在酿造端用AI模拟发酵、陈酿全过程，动态优化关键参数，完成原料分级、发酵控制和酒体风味数字化建模。以此，让每一批葡萄酒的口感更稳定、品质更可控。\n\n当然，让人工智能与广袤乡村实现更深层次的双向赋能，还有很长的路要走——\n\n一套AI系统，传感终端、边缘计算、决策模型全算上，初始投入比较大。普通农户能不能用得起、用得好？\n\n农业生产周期长、环境复杂、区域差异大，怎样才能收集利用一批高质量农业数据，推动AI模型迭代？\n\n下沉到村、懂技术懂农业的青年人才较为短缺，农业人工智能技术人才怎样引育？\n\n让“技术盆景”真正落地为“产业风景”，刘岳认为，关键是鼓励龙头企业、高校院所与新型农业经营主体协同攻关，围绕粮食、果蔬、畜牧、水产等优势产业，打造一批“听得懂农事、算得清账本、管得了全程”的行业大模型。\n\n此外，刘岳还建议，要更加重视构建与农村新质生产力相适应的农村新型生产关系，盘活整合农村各类资产资源，为充分发挥数字化、智能化技术进步作用提供保障。“推动农村数字化从‘局域网’向‘互联网’转化，要破除长期存在的‘信息孤岛’，构建乡村振兴的数据底座。”\n\n（大众新闻记者 于新悦 刘兵）\n\n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。  \n 本文来源：大众新闻·大众日报\n\n查看更多\n\n相关检索","大众新闻 2026-10-06","2026-10-06T00:00:00Z","报道",76,{"impact":321,"substance":1136,"depth":1137,"authority":385,"freshness":632,"relevant":455,"comment":1138},21,17,"省级农科院发布农业遥感大模型，含95%精度、2000万份样本等实质数据，信息增量与专业深度俱佳，值得进入每日精选。",[1140],{"name":1131,"url":1128},[1142,589,694,1143,1144],"数字乡村","农业大模型","遥感大模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[1147],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1149},{"candidates":1150,"agricultural_evidence":605},[1151,1156,1161,1166],{"code":32,"score":345,"supported":605,"title":1152,"abstract":1153,"tags":1155},[609],[609,1154],"估产",[],{"code":24,"score":689,"supported":605,"title":1157,"abstract":1159,"tags":1160},[1158],"大模型",[1158],[694,1143],{"code":16,"score":626,"supported":605,"title":1162,"abstract":1163,"tags":1165},[],[589,1164],"数字农业",[589],{"code":56,"score":455,"supported":581,"title":1167,"abstract":1168,"tags":1169},[],[],[1142],[1171,1172],"山东省农科院 舜耕粮安 遥感大模型","舜耕粮安 农业遥感 精度95%","山东省农科院舜耕粮安遥感大模型-4464","2026-10-09T00:04:56.436776Z",{"id":1176,"title":1177,"url":1178,"summary":1179,"summary_zh":1180,"content":11,"source_name":1181,"source_url":1178,"published_at":758,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":582,"score_detail":1182,"sources":1184,"tags":1186,"primary_topic":1190,"secondary_topics":1191,"classification":1194,"search_phrases":1216,"slug":1219,"view_count":408,"doi":1220,"paper":1221,"created_at":1238},4433,"PROSAIL-DNN-TL: A Hyperspectral Inversion Framework for Winter Wheat Leaf Chlorophyll Content Based on Fine-Tuned Transfer Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16191971","Accurate and efficient estimation of leaf chlorophyll content (LCC) is essential for crop physiological monitoring and precision agriculture. Current remote sensing approaches for LCC estimation mainly rely on machine learning and deep learning methods, which exhibit strong nonlinear modelling capabilities but require sufficient and representative samples. Radiative transfer models (RTMs) can simulate canopy spectral response based on physical mechanisms and generate large-scale datasets, thereby overcoming the limitations of purely data-driven approaches. However, their applications under diverse environmental conditions remain challenging due to uncertainties caused by model parameterization and discrepancies between simulated and observed data. This study proposed a hybrid framework, PROSAIL-DNN-TL, integrating the PROSAIL RTM with full-network fine-tuned transfer learning for winter wheat LCC estimation. First, 85,680 simulated samples generated by PROSAIL were used to pre-train a deep neural network (DNN), and field observations (n = 270) were subsequently used for full-network fine-tuning. A conventional DNN model trained only with field data and machine learning models based on different feature selection strategies were established for comparison. The results showed that: (1) UVE-selected bands were distributed across visible, red-edge, and near-infrared regions; CARS retained more bands in the visible region, whereas SPA mainly selected near-infrared bands; (2) RR and SVR achieved higher accuracy than KNN, with the full-spectrum and CARS feature sets showing superior performance and SPA showing the lowest accuracy; (3) PROSAIL-DNN-TL achieved reliable LCC estimation performance, with an average test R2 of 0.79 across 500 repeated experiments, and showed improved performance compared with conventional machine learning models; (4) PROSAIL-DNN-TL achieved reliable predictive performance when evaluated using datasets collected during the 2024 and 2025 growing seasons, with R2 values of 0.75 and 0.92, respectively. Overall, the proposed hybrid framework integrates physical knowledge from RTMs with data-driven modelling through transfer learning, providing a potential approach for crop chlorophyll monitoring. However, the SPAD-to-LCC conversion used in this study was not specifically calibrated for the instrument and crop, and therefore, the absolute LCC values should be interpreted with caution.","准确、高效地估算叶片叶绿素含量（leaf chlorophyll content，LCC）对作物生理监测和精准农业至关重要。当前遥感估算LCC的方法主要依赖机器学习和深度学习方法，这类方法具有较强的非线性建模能力，但需要充足且具有代表性的样本。辐射传输模型（radiative transfer models，RTMs）能够基于物理机制模拟冠层光谱响应并生成大规模数据集，从而克服纯数据驱动方法的局限性。然而，由于模型参数化带来的不确定性以及模拟数据与观测数据之间的差异，其在不同环境条件下的应用仍面临挑战。本研究提出了一种混合框架PROSAIL-DNN-TL，将PROSAIL辐射传输模型与全网络微调迁移学习相结合，用于冬小麦LCC估算。首先，利用PROSAIL生成的85 680个模拟样本预训练深度神经网络（deep neural network，DNN），随后利用田间观测数据（n = 270）进行全网络微调。同时建立了仅使用田间数据训练的传统DNN模型以及基于不同特征选择策略的机器学习模型作为对比。结果表明：（1）UVE选择的波段分布在可见光、红边和近红外区域；CARS在可见光区域保留了更多波段，而SPA主要选择近红外波段；（2）RR和SVR的精度高于KNN，全光谱和CARS特征集表现较优，SPA精度最低；（3）PROSAIL-DNN-TL取得了可靠的LCC估算性能，在500次重复实验中平均测试R²为0.79，且相比传统机器学习模型表现出更优的性能；（4）利用2024年和2025年生长季采集的数据集进行评估时，PROSAIL-DNN-TL均表现出可靠的预测性能，R²分别为0.75和0.92。总体而言，所提出的混合框架通过迁移学习将辐射传输模型的物理知识与数据驱动建模相结合，为作物叶绿素监测提供了一种潜在途径。然而，本研究中使用的SPAD到LCC的转换并未针对仪器和作物进行专门校准，因此绝对LCC值应谨慎解读。","Agronomy",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":866,"relevant":455,"comment":1183},"将PROSAIL辐射传输模型与深度神经网络迁移学习结合，用8万余模拟样本预训练、270个田间样本微调，冬小麦叶绿素反演精度可靠，方法新颖且数据规模扎实，对作物生理监测与精准农业有参考价值。",[1185],{"name":1181,"url":1178},[589,695,1187,1188,1189],"冬小麦","迁移学习","叶绿素监测",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1192,1193],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1195},{"candidates":1196,"agricultural_evidence":605},[1197,1203,1208,1212],{"code":216,"score":1198,"supported":605,"title":1199,"abstract":1201,"tags":1202},26,[1200,217],"winter wheat",[218,1200,217],[1187],{"code":32,"score":604,"supported":605,"title":1204,"abstract":1206,"tags":1207},[1205],"hyperspectral",[609,607],[695],{"code":24,"score":385,"supported":605,"title":1209,"abstract":1210,"tags":1211},[],[620,591,621,622],[1188],{"code":16,"score":626,"supported":605,"title":1213,"abstract":1214,"tags":1215},[],[697,890],[589],[1217,1218],"PROSAIL 冬小麦 叶绿素","高光谱 叶绿素 反演","PROSAIL冬小麦叶绿素-4433","10.3390\u002Fagronomy16191971",{"doi":1220,"openalex_id":1222,"authors":1223,"venue":1181,"cited_by_count":424,"oa_url":1178,"card":1233,"direction":674,"ingested_from":677},"W7220844556",[1224,1226,1228,1231],{"name":1225,"orcid":11},"Xueqing Zhu",{"name":1227,"orcid":11},"Ying Nian",{"name":1229,"orcid":1230},"Jikai Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2365-5221",{"name":1232,"orcid":11},"Xinwei Li",{"tldr":1234,"method":1235,"finding":1236,"direction":674,"opportunity":1237},"提出PROSAIL-DNN-TL框架，用PROSAIL模拟数据预训练DNN并全网络微调，反演冬小麦叶","PROSAIL辐射传输模型生成85680样本预训练DNN，270个田间样本全网络","500次重复实验平均测试R²为0.79，2024和2025年生长季验证R²分别为0.75和0.92，","SPAD到LCC转换未针对仪器和作物标定，可研究跨传感器、跨作物、跨生长季的迁移学习泛化与绝对叶绿素标定。","2026-10-08T23:30:29.383957Z",{"id":1240,"title":1241,"url":1242,"summary":1243,"summary_zh":1244,"content":11,"source_name":757,"source_url":1242,"published_at":758,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1245,"score_detail":1246,"sources":1248,"tags":1250,"primary_topic":1254,"secondary_topics":1255,"classification":1256,"search_phrases":1276,"slug":1279,"view_count":638,"doi":1280,"paper":1281,"created_at":1303},4432,"Horticulture acreage estimation: remote sensing vis-a-vis sample surveys","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74697-9","Timely and spatially explicit estimates of horticultural crop acreage are required to support agricultural statistics, resource allocation, and production planning. This study assessed the agreement between satellite-derived acreage estimates and house-to-house survey data for selected villages in Erode, Chittoor, and Nashik districts of India. Multispectral imagery from Resourcesat-2 LISS-IV and Sentinel-2 was subjected to radiometric and geometric correction, band stacking, mosaicking, and normalized difference vegetation index-based vegetation masking. Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms were evaluated for mapping banana, tapioca, mango, and onion. Classification performance varied among crops. MLC achieved overall accuracies of 84.83% for banana and 76.81% for mango, with kappa coefficients of 0.767 and 0.917, respectively. SAM produced an overall accuracy of 78.70% and a kappa coefficient of 0.797 for tapioca, whereas SVM performed best for onion, attaining an overall accuracy of 76.41% and a kappa coefficient of 0.713. Satellite-derived acreage estimates were strongly associated with survey estimates for banana (R 2 = 0.94), tapioca (R 2 = 0.93), and mango (R 2 = 0.94). Compared with the survey estimates, remote sensing underestimated the total areas of banana, tapioca, mango, and onion by 9.04%, 5.35%, 5.88%, and 26.08%, respectively. The results indicate that classification performance depends on crop structure, field size, spatial fragmentation, and classifier selection. Satellite imagery provided more reliable estimates for relatively large and contiguous orchards than for small and fragmented crop fields. Integrating satellite-based mapping with targeted field verification can therefore provide a scalable and spatially explicit framework for operational horticultural acreage estimation.","及时且具有空间明确性的园艺作物种植面积估算，是支撑农业统计、资源分配和生产规划的必要条件。本研究评估了卫星遥感估算面积与印度埃罗德、奇图尔和纳西克地区部分村庄逐户调查数据之间的一致性。对来自Resourcesat-2 LISS-IV和Sentinel-2的多光谱影像进行了辐射校正和几何校正、波段叠加、镶嵌以及基于归一化植被指数（NDVI）的植被掩膜处理。评估了最大似然分类（MLC）、光谱角制图（SAM）和支持向量机（SVM）算法在香蕉、木薯、芒果和洋葱制图中的应用效果。不同作物的分类性能存在差异。MLC对香蕉和芒果的总体精度分别为84.83%和76.81%，Kappa系数分别为0.767和0.917。SAM对木薯的总体精度为78.70%，Kappa系数为0.797；而SVM对洋葱的表现最佳，总体精度为76.41%，Kappa系数为0.713。卫星估算面积与调查估算值在香蕉（R²=0.94）、木薯（R²=0.93）和芒果（R²=0.94）上呈强相关。与调查估算值相比，遥感分别低估了香蕉、木薯、芒果和洋葱的总面积，低估幅度为9.04%、5.35%、5.88%和26.08%。结果表明，分类性能取决于作物结构、田块大小、空间破碎化程度和分类器选择。对于相对较大且连片的果园，卫星影像提供的估算值比小而破碎的农田更为可靠。因此，将基于卫星的制图与有针对性的实地验证相结合，可为业务化园艺种植面积估算提供一个可扩展且具有空间明确性的框架。",79,{"impact":865,"substance":321,"depth":361,"authority":345,"freshness":866,"relevant":455,"comment":1247},"该研究以多光谱遥感与入户调查对比估算印度园艺作物面积，方法扎实、数据具体，对农业遥感统计具有参考价值，但属区域性案例，影响力有限。",[1249],{"name":757,"url":1242},[592,1251,273,1252,1253],"印度农业","作物面积估算","农业统计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1257},{"candidates":1258,"agricultural_evidence":605},[1259,1263,1268,1272],{"code":32,"score":361,"supported":605,"title":1260,"abstract":1261,"tags":1262},[607],[609,607,706],[592],{"code":286,"score":638,"supported":605,"title":1264,"abstract":1265,"tags":1267},[],[1266],"芒果",[],{"code":271,"score":455,"supported":581,"title":1269,"abstract":1270,"tags":1271},[],[],[273],{"code":556,"score":455,"supported":581,"title":1273,"abstract":1274,"tags":1275},[],[],[1251],[1277,1278],"Resourcesat-2 Sentinel-2 园艺作物 面积估算","Erode Chittoor Nashik 遥感 抽样调查","Resourcesat-2Sentinel-2园艺作物面积估算-4432","10.1038\u002Fs41598-026-74697-9",{"doi":1280,"openalex_id":1282,"authors":1283,"venue":757,"cited_by_count":424,"oa_url":1242,"card":1298,"direction":674,"ingested_from":677},"W7220842723",[1284,1287,1289,1292,1295],{"name":1285,"orcid":1286},"Tauqueer Ahmad","https:\u002F\u002Forcid.org\u002F0000-0002-2573-9667",{"name":1288,"orcid":11},"Prachi Misra Sahoo",{"name":1290,"orcid":1291},"A.K. Biswas","https:\u002F\u002Forcid.org\u002F0000-0002-1708-7363",{"name":1293,"orcid":1294},"Gopal Krishna","https:\u002F\u002Forcid.org\u002F0000-0002-1070-9916",{"name":1296,"orcid":1297},"Karunesh K. Shukla","https:\u002F\u002Forcid.org\u002F0000-0001-8537-2433",{"tldr":1299,"method":1300,"finding":1301,"direction":674,"opportunity":1302},"对比卫星遥感与入户调查的园艺作物面积估算，评估分类算法精度与面积一致性。","用Resourcesat-2 LISS-IV和Sentinel-2影像，比较ML","遥感对连片大果园估算可靠（R²约0.94），但普遍低估面积，洋葱低估达26%。","可研究碎片化小农户地块的遥感面积校正方法，结合抽样验证提升园艺作物统计精度。","2026-10-08T23:30:29.318866Z",{"id":1305,"title":1306,"url":1307,"summary":1308,"summary_zh":1309,"content":11,"source_name":1310,"source_url":1307,"published_at":758,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1311,"score_detail":1312,"sources":1314,"tags":1316,"primary_topic":1320,"secondary_topics":1321,"classification":1322,"search_phrases":1352,"slug":1355,"view_count":69,"doi":1356,"paper":1357,"created_at":1377},4418,"Recent Developments in Smart Agriculture and Horticulture: A Critical Appraisal of Sensing, Robotics, Analytics and Controlled-Environment Production","https:\u002F\u002Fdoi.org\u002F10.9734\u002Facri\u002F2026\u002Fv26i102207","Digital and automated technologies have been promoted as a route to higher productivity, lower input use and improved resilience in both field agriculture and horticulture, yet the evidence underpinning these expectations remains uneven and, in several domains, contested. This review critically examines developments in smart agriculture and horticulture across six interdependent technological domains: proximal and remote sensing, machine-learning analytics, field and glasshouse robotics, controlled-environment production, postharvest quality assessment and chain integrity, and the integration architectures that connect them. Literature was identified through structured searching of open scholarly databases and indexes, supplemented by backward and forward citation tracking and by targeted retrieval from institutional sources, with critical appraisal focused on methodological adequacy, ecological validity and the correspondence between reported performance and operational conditions. Three findings dominate the synthesis. First, the technical performance of sensing and recognition systems is frequently established under conditions that differ materially from commercial practice, and the resulting laboratory-to-field generalisation deficit is the single most consistent methodological weakness across the literature. Second, horticultural systems impose constraints that arable-derived technologies do not resolve automatically, including three-dimensional canopy occlusion, delicate and heterogeneous produce, high crop value per unit area and dense, frequently repeated operations; the most convincing advances involve co-design of the crop system and the machine rather than the machine alone. Third, evidence on economic and environmental outcomes is considerably weaker than evidence on technical feasibility, with profitability strongly conditional on within-field variability, holding size, labour costs and energy prices, and with sustainability claims for controlled-environment production remaining sensitive to assumptions about electricity supply and system boundaries. Substantial gaps persist in multi-season field validation, transparent reporting of failure modes, distributional analysis of who benefits from digitalisation, and governance of agricultural data. Confidence in current conclusions is constrained by geographical concentration of evidence and by the rapid obsolescence of technology-specific findings.","数字与自动化技术已被推崇为提高大田农业和园艺作物生产力、降低投入品使用并增强韧性的路径，但支撑这些预期的证据仍不均衡，且在若干领域存在争议。本综述批判性地考察了智慧农业与园艺在六个相互依赖的技术领域中的进展：近端与遥感、机器学习分析、田间与温室机器人、受控环境生产、采后品质评估与供应链完整性，以及连接上述各环节的集成架构。文献通过结构化检索开放学术数据库与索引获得，并辅以引文回溯与追踪以及从机构来源进行定向检索，批判性评价聚焦于方法学充分性、生态效度以及所报告性能与实际运行条件之间的对应关系。综合结果中呈现三项主要发现。第一，传感与识别系统的技术性能往往在与商业实践存在实质性差异的条件下确立，由此产生的“实验室到田间”泛化差距是文献中最一致的方法学弱点。第二，园艺系统施加的约束并不能由源自大田作物的技术自动解决，包括三维冠层遮挡、娇嫩且异质的农产品、单位面积作物价值高以及密集且频繁重复的作业；最具说服力的进展涉及作物系统与机器的协同设计，而非仅靠机器本身。第三，关于经济与环境结果的证据明显弱于关于技术可行性的证据，盈利能力强烈取决于田内变异性、经营规模、劳动力成本和能源价格，而受控环境生产的可持续性主张仍对电力供应和系统边界的假设高度敏感。在多季节田间验证、失败模式的透明报告、数字化受益者的分布分析以及农业数据治理方面仍存在显著空白。当前结论的可信度受到证据地理集中以及技术特定发现迅速过时的制约。","Archives of Current Research International",78,{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":173,"relevant":455,"comment":1313},"系统评述智慧农业与园艺六大技术域的综述论文，指出实验室到田间泛化不足、经济环境证据薄弱等关键缺口，学术增量扎实但属国际综述、非国内政策或产业事件。",[1315],{"name":1310,"url":1307},[589,695,1317,1318,1319],"农业机器人","设施园艺","农业数据治理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1323},{"candidates":1324,"agricultural_evidence":605},[1325,1330,1334,1339,1343,1348],{"code":16,"score":604,"supported":605,"title":1326,"abstract":1328,"tags":1329},[1327],"smart agriculture",[589,1327],[589],{"code":32,"score":626,"supported":605,"title":1331,"abstract":1332,"tags":1333},[],[609,607],[695],{"code":324,"score":400,"supported":605,"title":1335,"abstract":1336,"tags":1338},[],[1337],"温室",[1318],{"code":24,"score":638,"supported":605,"title":1340,"abstract":1341,"tags":1342},[],[591],[],{"code":56,"score":638,"supported":605,"title":1344,"abstract":1345,"tags":1347},[],[1346],"agricultural data",[],{"code":120,"score":455,"supported":581,"title":1349,"abstract":1350,"tags":1351},[],[],[1317],[1353,1354],"农业数据治理 农业机器人 农业遥感 智慧农业","农业数据治理 农业机器人","农业数据治理农业机器人农业遥感智慧农业-4418","10.9734\u002Facri\u002F2026\u002Fv26i102207",{"doi":1356,"openalex_id":1358,"authors":1359,"venue":1310,"cited_by_count":424,"oa_url":1307,"card":1372,"direction":676,"ingested_from":677},"W7220849440",[1360,1362,1364,1366,1368,1370],{"name":1361,"orcid":11},"Devesh Arya",{"name":1363,"orcid":11},"D. Swami",{"name":1365,"orcid":11},"M. C. Vinodraj",{"name":1367,"orcid":11},"Vivek Kumar Pathak",{"name":1369,"orcid":11},"Punabati Heisnam",{"name":1371,"orcid":11},"Abhinash Moirangthem",{"tldr":1373,"method":1374,"finding":1375,"direction":676,"opportunity":1376},"综述智慧农业与园艺在传感、机器人、分析及可控环境生产方面的进展，并批判性评估其证据质量。","结构化文献检索与引文追踪，重点评估方法学、生态效度及实际适用性。","技术性能多在非商业条件下验证，经济环境证据弱，园艺需作物-机器协同设计。","需多季田间验证、失败模式透明报告、数字化受益分布分析及农业数据治理研究。","2026-10-08T23:30:09.539116Z",{"id":1379,"title":1380,"url":1381,"summary":1382,"summary_zh":1383,"content":11,"source_name":1384,"source_url":1381,"published_at":1385,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1386,"score_detail":1387,"sources":1389,"tags":1391,"primary_topic":1395,"secondary_topics":1396,"classification":1398,"search_phrases":1417,"slug":1420,"view_count":424,"doi":1421,"paper":1422,"created_at":1457},4366,"Article topic: comprehensive assessment of soil and flora contamination by heavy metals in the zone of influence of the Nikolaevskaya enrichment plant","https:\u002F\u002Fdoi.org\u002F10.51886\u002F1999-740x_2026_3_46","Agricultural lands in East Kazakhstan region cover a significant area, with large portions of pasture and cropland situated in close proximity to operating industrial facilities. Despite this, no comprehensive integrated assessment combining remote sensing, ground-based soil monitoring, and flora survey had previously been conducted for the zone of influence of the Nikolaevskaya enrichment plant. This study aimed to fill that gap by a 40-year land cover analysis (1985-2024) using publicly available satellite data processed in Google Earth Engine with soil sampling and vegetation surveys across 17 sites. Soil analyses revealed not only elevated heavy metal concentrations but also a sharp pH decrease, conditions under which heavy metals become significantly more mobile and available for plant uptake. Technosol formation was observed at multiple sampling points, indicating a permanent transformation of the natural soil profile. The flora survey recorded 72 species from 62 genera, with ruderal and synanthropic species domina-ting disturbed habitats and native species persisting only at control points more distant from the facility. Taken together, these findings point to ongoing ecosystem degradation and raise concrete concerns for the regional food chain given the pastureland and cropland adjacent to the plant.","东哈萨克斯坦地区的农业用地面积广阔，其中大片牧场和农田与正在运营的工业设施相距不远。尽管如此，此前尚未针对尼古拉耶夫斯克选矿厂影响区域开展过将遥感、地面土壤监测与植物区系调查相结合的综合评估。本研究旨在填补这一空白，通过为期40年的土地覆盖分析（1985—2024年），利用Google Earth Engine处理的公开卫星数据，并结合17个样点的土壤采样与植被调查。土壤分析不仅揭示了重金属浓度升高，还发现pH值急剧下降，在这种条件下，重金属的迁移性和植物可利用性显著增强。在多个采样点观察到技术土（Technosol）的形成，表明自然土壤剖面发生了永久性改变。植物区系调查共记录到62属72种植物，其中扰动生境以杂草类和伴人植物物种为主，原生物种仅存于距设施较远的对照点。综合来看，这些发现表明生态系统正在持续退化，并且鉴于该厂邻近牧场和农田，对区域食物链构成了切实的担忧。","Pochvovedenie i agrokhimiya","2026-10-05T00:00:00Z",73,{"impact":416,"substance":321,"depth":361,"authority":689,"freshness":173,"relevant":455,"comment":1388},"结合40年遥感与地面采样的重金属污染综合评估，方法扎实、结论具体，但属区域案例研究，公共影响有限。",[1390],{"name":1384,"url":1381},[1392,646,592,1393,1394],"耕地保护","Google Earth Engine","土壤重金属",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[1397],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1399},{"candidates":1400,"agricultural_evidence":605},[1401,1405,1409,1413],{"code":144,"score":604,"supported":605,"title":1402,"abstract":1403,"tags":1404},[145],[635,145],[1394],{"code":32,"score":173,"supported":605,"title":1406,"abstract":1407,"tags":1408},[],[609,607],[1393,592],{"code":136,"score":455,"supported":581,"title":1410,"abstract":1411,"tags":1412},[],[],[1392],{"code":176,"score":455,"supported":581,"title":1414,"abstract":1415,"tags":1416},[],[],[646],[1418,1419],"Nikolaevskaya 选矿厂 土壤污染","东哈萨克斯坦 农田 重金属","Nikolaevskaya选矿厂土壤污染-4366","10.51886\u002F1999-740x_2026_3_46",{"doi":1421,"openalex_id":1423,"authors":1424,"venue":1384,"cited_by_count":424,"oa_url":1450,"card":1451,"direction":674,"ingested_from":677},"W7220713350",[1425,1428,1430,1433,1436,1439,1442,1445,1448],{"name":1426,"orcid":1427},"Yuliya Tugunova","https:\u002F\u002Forcid.org\u002F0009-0006-1037-1480",{"name":1429,"orcid":11},"M. Altynbayeva",{"name":1431,"orcid":1432},"Жанар Абилда","https:\u002F\u002Forcid.org\u002F0000-0001-8505-5243",{"name":1434,"orcid":1435},"Рахим Канат","https:\u002F\u002Forcid.org\u002F0009-0009-2418-5920",{"name":1437,"orcid":1438},"Abylay Begaly","https:\u002F\u002Forcid.org\u002F0009-0009-2848-336X",{"name":1440,"orcid":1441},"D. Kairkhanov","https:\u002F\u002Forcid.org\u002F0009-0006-1017-1980",{"name":1443,"orcid":1444},"Iskander Isgandarov","https:\u002F\u002Forcid.org\u002F0009-0001-4386-3303",{"name":1446,"orcid":1447},"ELENA A. ISAKOVA","https:\u002F\u002Forcid.org\u002F0000-0002-9056-6504",{"name":1449,"orcid":11},"Kh. Choi","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1135\u002F802",{"tldr":1452,"method":1453,"finding":1454,"direction":1455,"opportunity":1456},"综合遥感、土壤采样与植被调查，评估哈萨克斯坦一选矿厂影响区40年重金属污染与生态退化。","Google Earth Engine处理1985-2024卫星数据，结合17个","土壤重金属升高且pH骤降，重金属迁移性增强，形成技术土，植被以杂草为主，威胁食物链。","农业绿色发展与碳","可延伸研究重金属活化与pH耦合机制，并构建污染区农田食物链风险预警模型。","2026-10-07T23:30:29.146414Z",{"id":1459,"title":1460,"url":1461,"summary":1462,"summary_zh":1463,"content":11,"source_name":1464,"source_url":1461,"published_at":1385,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1465,"score_detail":1466,"sources":1468,"tags":1470,"primary_topic":1473,"secondary_topics":1474,"classification":1476,"search_phrases":1491,"slug":1494,"view_count":69,"doi":1495,"paper":1496,"created_at":1510},4365,"An Index-Based Algorithm for Detecting Water Soil Erosion Indicators from Sentinel-2 Multispectral Data and Its Verification on Field Data of Rostov Oblast and Krasnodar Krai","https:\u002F\u002Fdoi.org\u002F10.23947\u002F2587-8999-2026-10-3-49-57","Introduction . The paper presents the development and testing of an index-based algorithm for detecting indicators of soil erosion by water from Sentinel-2 remote sensing data, considered as the first practical stage in constructing a hybrid physics-based mass-transport model with neural-network parameter identification. Materials and Methods . The algorithm relies on joint analysis of time series of the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), followed by spatial change detection (ΔBSI) with threshold filtering at the 90th percentile. The algorithm was first validated on synthetic data and then applied to real Sentinel-2 imagery for two test sites – the Kamensky District of Rostov Oblast and the vicinity of Moldovanskoye village, Krymsky District, Krasnodar Krai – covering 10 dates within 2024. Results. The algorithm is shown to consistently identify spatially localised zones of increasing bare-soil exposure. Discussion . A key limitation – the inability to distinguish erosion-related exposure from ordinary agrotechnical bare soil — is discussed, along with directions for resolving it (digital elevation model overlay, multi-year analysis). Conclusion . An index-based algorithm for detecting indicators of water-induced soil erosion from Sentinel-2 multispectral time series was developed and validated. Verification on synthetic data and application to two real test areas in southern Russia confirmed its operability while demonstrating the need to separate erosion-related changes from agricultural soil exposure in subsequent model development.","引言。本文介绍了一种基于指数的算法开发与测试，该算法用于从Sentinel-2遥感数据中检测水蚀土壤侵蚀指标，被视为构建基于物理的质量输运混合模型并结合神经网络参数辨识的第一个实践阶段。材料与方法。该算法依赖于对归一化植被指数（NDVI）和裸土指数（BSI）时间序列的联合分析，随后进行空间变化检测（ΔBSI），并在第90百分位数处进行阈值滤波。该算法首先在合成数据上进行了验证，然后应用于两个测试点的真实Sentinel-2影像——罗斯托夫州卡缅斯基区和克拉斯诺达尔边疆区克里姆斯克区摩尔多瓦诺夫斯科耶村附近——覆盖2024年内的10个日期。结果。结果表明，该算法能够一致地识别出裸土暴露增加的空间局部区域。讨论。讨论了关键局限性——无法区分与侵蚀相关的裸土暴露和普通农技作业造成的裸土——以及解决该问题的方向（叠加数字高程模型、多年分析）。结论。开发并验证了一种基于指数的算法，用于从Sentinel-2多光谱时间序列中检测水蚀土壤侵蚀指标。在合成数据上的验证以及在俄罗斯南部两个真实测试区域的应用证实了其可操作性，同时表明在后续模型开发中有必要将与侵蚀相关的变化与农业裸土暴露区分开来。","COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES",70,{"impact":416,"substance":508,"depth":1137,"authority":689,"freshness":173,"relevant":455,"comment":1467},"提出基于NDVI与BSI时序的土壤水蚀指标遥感识别算法，并在俄罗斯南部两个试验区验证，方法有新意但属区域尺度研究，对国内农业遥感监测有参考价值。",[1469],{"name":1464,"url":1461},[589,1471,1080,592,1472],"Sentinel-2","土壤侵蚀",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[1475],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1477},{"candidates":1478,"agricultural_evidence":605},[1479,1483,1487],{"code":32,"score":508,"supported":605,"title":1480,"abstract":1481,"tags":1482},[706],[609,607,706],[1080,1471,592],{"code":144,"score":345,"supported":605,"title":1484,"abstract":1485,"tags":1486},[145],[635,145],[],{"code":16,"score":455,"supported":581,"title":1488,"abstract":1489,"tags":1490},[],[],[589],[1492,1493],"Sentinel-2 土壤侵蚀 遥感","罗斯托夫州 克拉斯诺达尔 土壤侵蚀","Sentinel-2土壤侵蚀遥感-4365","10.23947\u002F2587-8999-2026-10-3-49-57",{"doi":1495,"openalex_id":1497,"authors":1498,"venue":1464,"cited_by_count":424,"oa_url":1461,"card":1505,"direction":674,"ingested_from":677},"W7220651513",[1499,1502],{"name":1500,"orcid":1501},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":1503,"orcid":1504},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",{"tldr":1506,"method":1507,"finding":1508,"direction":674,"opportunity":1509},"开发基于Sentinel-2时序NDVI与BSI指数的水土流失指标检测算法，并在俄罗斯南部实地验证。","联合NDVI与BSI时序，用ΔBSI阈值（90百分位）检测裸土变化，合成与真实数","算法能稳定识别裸土暴露增加区域，但无法区分侵蚀与农事裸土，需DEM和多年度分析。","可融合DEM与多年度时序，构建区分侵蚀与农事裸土的分类模型，并接入神经网络参数识别。","2026-10-07T23:30:29.080156Z",{"id":1512,"title":1513,"url":1514,"summary":1515,"summary_zh":1516,"content":11,"source_name":1517,"source_url":1514,"published_at":1132,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":1518,"sources":1520,"tags":1522,"primary_topic":1525,"secondary_topics":1526,"classification":1527,"search_phrases":1539,"slug":1542,"view_count":455,"doi":1543,"paper":1544,"created_at":1577},4357,"Evaluating When and Where TROPOMI XCH4 Uncertainties Yield Consistent Flux Estimation over East, South, and Central Asia","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-4468","Abstract. Satellite remote sensing of methane (CH4) offers the potential to monitor emissions across large scales, but skepticism remains among regulators, policymakers, and the scientific community of both the precision and accuracy of such estimations due to unquantified observational uncertainties. Using 500 perturbation experiments with four error distributions applied to TROPOMI XCH4 over East, South, and Central Asia, we systematically assess where and when divergence‑based emission estimates are reliable. We show that the shape of the uncertainty distribution – not just its magnitude – governs error propagation and thus effects the stability of derived fluxes. Furthermore we show that uncertainty propogates over both space and time, indicating that purely local accounting may lead to an underestimate of uncertainty. Coal‑dominated industrial grids retain trustworthy signals (30% retention of valid perturbations), whereas agricultural and mixed‑use grids retain only 16%, indicating that current filtering practices are insufficient for these land types. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions, directly supporting the Global Methane Pledge and national greenhouse gas inventories by identifying where satellite data can be trusted (coal‑dominated regions) and where additional caution or alternative approaches are needed. The process is data-neutral and can be extended to other satellite platforms and adopted by other regions around the globe.","摘要：卫星遥感甲烷（CH₄）具有在大尺度上监测排放的潜力，但由于观测不确定性尚未量化，监管机构、政策制定者和科学界对此类估算的精度和准确度仍存疑虑。本研究利用500组扰动实验，将四种误差分布应用于东亚、南亚和中亚地区的TROPOMI XCH₄数据，系统评估了基于散度（divergence）的排放估算在何时何地是可靠的。研究表明，不确定性分布的形状——而不仅仅是其量级——决定了误差传播，进而影响所推导通量的稳定性。此外，不确定性在空间和时间上均会传播，这表明纯粹的局地核算可能导致对不确定性的低估。以煤炭为主的工业网格保留了可信信号（有效扰动的保留率为30%），而农业和混合用途网格仅保留16%，表明当前的滤波方法对这些土地类型而言尚不充分。这些结果为卫星反演的甲烷排放提供了一种实用的、考虑不确定性的滤波方案，通过识别卫星数据可信的区域（以煤炭为主的地区）以及需要额外谨慎或替代方法的区域，直接支持全球甲烷承诺（Global Methane Pledge）和国家温室气体清单的编制。该方法对数据具有中立性，可推广至其他卫星平台并被全球其他地区采用。","OpenAlex",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":1519},"该论文聚焦卫星甲烷排放通量估算不确定性，属环境遥感领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[1521],{"name":1517,"url":1514},[592,1523,1524],"甲烷排放","卫星数据",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1528},{"candidates":1529,"agricultural_evidence":605},[1530,1534],{"code":32,"score":626,"supported":605,"title":1531,"abstract":1532,"tags":1533},[],[609,607],[592],{"code":324,"score":632,"supported":605,"title":1535,"abstract":1536,"tags":1538},[],[1337,1537],"greenhouse",[],[1540,1541],"TROPOMI XCH4 甲烷","卫星遥感 甲烷排放","TROPOMIXCH4甲烷-4357","10.5194\u002Fegusphere-2026-4468",{"doi":1543,"openalex_id":1545,"authors":1546,"venue":11,"cited_by_count":424,"oa_url":1514,"card":1572,"direction":674,"ingested_from":677},"W7220708326",[1547,1550,1553,1556,1558,1561,1564,1567,1569],{"name":1548,"orcid":1549},"Wei Xin Hu","https:\u002F\u002Forcid.org\u002F0000-0002-8780-7362",{"name":1551,"orcid":1552},"Jason Blake Cohen","https:\u002F\u002Forcid.org\u002F0000-0002-9889-8175",{"name":1554,"orcid":1555},"Lingxiao Lu","https:\u002F\u002Forcid.org\u002F0000-0002-3839-9083",{"name":1557,"orcid":11},"Bo Zheng",{"name":1559,"orcid":1560},"Pravash Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-4770-4526",{"name":1562,"orcid":1563},"Simone Lolli","https:\u002F\u002Forcid.org\u002F0000-0001-6111-152X",{"name":1565,"orcid":1566},"Andrea Garzelli","https:\u002F\u002Forcid.org\u002F0000-0003-2332-780X",{"name":1568,"orcid":11},"Jun Li",{"name":1570,"orcid":1571},"Kai Qin","https:\u002F\u002Forcid.org\u002F0000-0002-1280-6330",{"tldr":1573,"method":1574,"finding":1575,"direction":674,"opportunity":1576},"用500组扰动实验评估TROPOMI XCH4在亚洲三区甲烷通量估算的可靠性。","对TROPOMI XCH4施加四种误差分布的500次扰动实验，分析通量反演稳定性","不确定性分布形状而非大小主导误差传播，且跨时空传播；煤炭区信号可信度30%，农业混合区仅16%。","可发展面向农业混合区的误差感知滤波与替代反演方法，并推广至其他卫星平台与区域。","2026-10-07T23:30:26.011458Z",{"id":1579,"title":1580,"url":1581,"summary":1582,"summary_zh":1583,"content":11,"source_name":1584,"source_url":1581,"published_at":1132,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":937,"score_detail":1585,"sources":1587,"tags":1589,"primary_topic":1591,"secondary_topics":1592,"classification":1594,"search_phrases":1617,"slug":1620,"view_count":400,"doi":1621,"paper":1622,"created_at":1650},4356,"Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsae2.70218","ABSTRACT Accurate mapping of rice, one of the world's most important food crops, is critical for monitoring its growth, estimating yields, and supporting global food security. Most deep learning methods for crop classification rely on multi‐temporal optical remote sensing data. However, it is difficult to obtain sufficient ground‐truth samples for time‐series deep learning models through conducting large‐scale field surveys, and persistent cloud and rainfall often limit optical observations during the rice‐growing seasons in subtropical regions. Rice classification based solely on time‐series optical images may therefore be less accurate and reliable. Here, we developed two dual‐branch deep learning networks for effective rice identification: one combining a long short‐term memory network running in parallel with a multi‐layer perceptron (LSTM + MLP), and the other replacing the LSTM with a one‐dimensional convolutional neural network (Conv1d + MLP). These models process temporal sequences in parallel with auxiliary data through the MLP, allowing them to automatically extract phenology information while incorporating auxiliary variables such as texture features, digital elevation model (DEM) data, and the standard deviation of the vegetation index time series. We evaluated these models in a representative county in the subtropical region of China, and compared the accuracies with standalone LSTM, Conv1d, MLP, and random forest (RF) models. The results revealed that LSTM + MLP had the highest accuracy, with an F1‐score of 95.5%, which can automatically extract rice growth patterns and successfully identify the key phenological information of flood signals during the rice transplanting period. By incorporating auxiliary variables, the model achieved an average accuracy improvement of 8.1% relative to the models using only temporal features. The proposed method provides a useful approach for rice mapping in subtropical regions.","准确绘制水稻这一全球最重要的粮食作物之一，对于监测其生长、估算产量以及保障全球粮食安全至关重要。大多数作物分类的深度学习方法依赖于多时相光学遥感数据。然而，通过开展大规模实地调查来获取足够的地面真值样本以用于时间序列深度学习模型十分困难，而且亚热带地区水稻生长季期间持续的云层和降雨往往限制了光学观测。因此，仅基于时间序列光学图像的水稻分类可能精度较低且可靠性不足。本研究开发了两种双分支深度学习网络以实现有效的水稻识别：一种将长短期记忆网络与多层感知机并行运行（LSTM + MLP），另一种则用一维卷积神经网络替代LSTM（Conv1d + MLP）。这些模型通过MLP将时间序列与辅助数据并行处理，使其能够自动提取物候信息，同时纳入纹理特征、数字高程模型（DEM）数据以及植被指数时间序列标准差等辅助变量。我们在中国亚热带地区一个代表性县对这些模型进行了评估，并将精度与单独的LSTM、Conv1d、MLP和随机森林（RF）模型进行了比较。结果表明，LSTM + MLP精度最高，F1分数达到95.5%，能够自动提取水稻生长模式，并成功识别水稻移栽期洪水信号的关键物候信息。通过纳入辅助变量，该模型相对于仅使用时间特征的模型平均精度提高了8.1%。所提出的方法为亚热带地区水稻制图提供了一种有效途径。","Journal of Sustainable Agriculture and Environment",{"impact":604,"substance":1136,"depth":1137,"authority":689,"freshness":866,"relevant":455,"comment":1586},"提出双分支深度学习网络融合时序与辅助数据，水稻识别F1达95.5%，方法新颖且结论可靠，对亚热带多云雨区作物遥感制图有实用价值。",[1588],{"name":1584,"url":1581},[589,694,210,609,1590],"作物分类",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1593],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1595},{"candidates":1596,"agricultural_evidence":605},[1597,1601,1605,1609,1613],{"code":208,"score":604,"supported":605,"title":1598,"abstract":1599,"tags":1600},[209],[210,209],[210],{"code":32,"score":173,"supported":605,"title":1602,"abstract":1603,"tags":1604},[],[609,607],[1590,609],{"code":24,"score":626,"supported":605,"title":1606,"abstract":1607,"tags":1608},[],[620,622],[694],{"code":263,"score":632,"supported":605,"title":1610,"abstract":1611,"tags":1612},[],[590,629],[],{"code":16,"score":455,"supported":581,"title":1614,"abstract":1615,"tags":1616},[],[],[589],[1618,1619],"水稻 遥感 分类 深度学习","亚热带 水稻 制图","水稻遥感分类深度学习-4356","10.1002\u002Fsae2.70218",{"doi":1621,"openalex_id":1623,"authors":1624,"venue":1584,"cited_by_count":424,"oa_url":1644,"card":1645,"direction":674,"ingested_from":677},"W7220691059",[1625,1627,1630,1633,1635,1638,1641],{"name":1626,"orcid":11},"Rongfei Ma",{"name":1628,"orcid":1629},"Wenjiao Shi","https:\u002F\u002Forcid.org\u002F0000-0001-6199-9733",{"name":1631,"orcid":1632},"Minglei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4174-9224",{"name":1634,"orcid":11},"Xiwei Chen",{"name":1636,"orcid":1637},"Chenchen Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0458-6232",{"name":1639,"orcid":1640},"Baihong Pan","https:\u002F\u002Forcid.org\u002F0009-0004-1100-6798",{"name":1642,"orcid":1643},"Xiangming Xiao","https:\u002F\u002Forcid.org\u002F0000-0003-0956-7428","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fsae2.70218",{"tldr":1646,"method":1647,"finding":1648,"direction":674,"opportunity":1649},"提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。","LSTM\u002FConv1d与MLP并行双分支网络，融合时序植被指数、纹理、DEM等辅","LSTM+MLP模型F1达95.5%，辅助变量使精度平均提升8.1%，能识别插秧期淹水信号。","可探索多源遥感与气象数据融合，解决多云雨地区时序缺失下的作物精细制图问题。","2026-10-07T23:30:25.945309Z",{"id":1652,"title":1653,"url":1654,"summary":1655,"summary_zh":1656,"content":11,"source_name":1517,"source_url":1654,"published_at":1132,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1245,"score_detail":1657,"sources":1659,"tags":1661,"primary_topic":1665,"secondary_topics":1666,"classification":1667,"search_phrases":1690,"slug":1693,"view_count":69,"doi":1694,"paper":1695,"created_at":1712},4355,"Agricultural Flood Damage Assessment: A Systematic Review of Methods, Coverage, and Integration Gaps","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-3701","Abstract. Agricultural flooding causes tens of billions of dollars in annual losses globally, yet the methods used to assess these losses systematically capture only a fraction of their true economic scope. This systematic review synthesizes 65 peer-reviewed studies published between 1984 and 2025 to evaluate how agricultural flood damage is measured, what damage categories are covered, and where critical methodological gaps persist. We find a structural asymmetry across the reviewed literature – all 65 reviewed studies address direct immediate damages – primarily crop yield loss estimated through depth-damage functions or remote sensing – while direct induced effects appear in only 8 studies, indirect damages in 9, and intangible damages in fewer than 5. This asymmetry has persisted for four decades, through a major remote sensing revolution, because the dominant methods are structurally incapable of measuring damage categories beyond direct immediate losses. We introduce a Methods-by-Damage-Category Matrix that maps eight assessment methods against five damage categories, making the coverage gap visible and providing a diagnostic framework for method selection. Three post-2013 damage frontiers – long-term land productivity degradation, supply chain finance disruption, and farmer mental health – are identified as emerging categories warranting dedicated methodological development. Existing agricultural flood damage estimates used in benefit–cost analyses and disaster compensation are systematically downward-biased; closing this bias requires integrating remote sensing-based physical detection with economic valuation methods capable of capturing the full damage taxonomy.","摘要：农业洪水每年在全球造成数百亿美元损失，然而用于系统评估这些损失的方法仅能捕捉其真实经济影响的一小部分。本系统综述综合了1984年至2025年间发表的65项同行评审研究，评估农业洪水损失如何被测量、涵盖了哪些损失类别，以及关键方法论空白存在于何处。我们发现，所综述文献中存在结构性不对称——全部65项研究均涉及直接即时损失，主要是通过深度-损失函数或遥感估算的作物产量损失——而直接诱发效应仅出现在8项研究中，间接损失出现在9项研究中，无形损失则不足5项。这种不对称已持续四十年，历经遥感技术的重大革命，因为主流方法在结构上无法测量直接即时损失之外的损失类别。我们引入“方法-损失类别矩阵”，将八种评估方法与五类损失类别进行对应，使覆盖缺口清晰可见，并为方法选择提供诊断框架。2013年后的三个损失前沿——长期土地生产力退化、供应链金融中断和农民心理健康——被识别为值得专门方法论开发的新兴类别。现有用于成本效益分析和灾害补偿的农业洪水损失估算存在系统性低估；弥合这一偏差需要将基于遥感的物理检测与能够捕捉完整损失分类的经济估值方法相结合。",{"impact":361,"substance":321,"depth":361,"authority":689,"freshness":173,"relevant":455,"comment":1658},"系统综述揭示农业洪灾损失评估长期偏低估的方法学缺口，提出方法-损失类别矩阵，对灾害补偿与成本收益分析有实质参考价值。",[1660],{"name":1517,"url":1654},[1662,590,1663,592,1664],"农业信息化","农业保险","灾害评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1668},{"candidates":1669,"agricultural_evidence":605},[1670,1674,1678,1682,1686],{"code":32,"score":626,"supported":605,"title":1671,"abstract":1672,"tags":1673},[],[609,607],[592],{"code":263,"score":455,"supported":581,"title":1675,"abstract":1676,"tags":1677},[],[],[590],{"code":503,"score":455,"supported":581,"title":1679,"abstract":1680,"tags":1681},[],[],[1663],{"code":184,"score":455,"supported":581,"title":1683,"abstract":1684,"tags":1685},[],[],[1664],{"code":16,"score":455,"supported":581,"title":1687,"abstract":1688,"tags":1689},[],[],[1662],[1691,1692],"农业洪涝灾害 损失评估","遥感 农作物 灾害损失","农业洪涝灾害损失评估-4355","10.5194\u002Fegusphere-2026-3701",{"doi":1694,"openalex_id":1696,"authors":1697,"venue":11,"cited_by_count":424,"oa_url":1654,"card":1707,"direction":674,"ingested_from":677},"W7220668753",[1698,1701,1704],{"name":1699,"orcid":1700},"Shokhrukh–Mirzo Jalilov","https:\u002F\u002Forcid.org\u002F0000-0002-2428-9094",{"name":1702,"orcid":1703},"Haluk Gedikoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-0158-2597",{"name":1705,"orcid":1706},"Robert Maltsbarger","https:\u002F\u002Forcid.org\u002F0009-0003-7801-8390",{"tldr":1708,"method":1709,"finding":1710,"direction":674,"opportunity":1711},"系统综述65项农业洪水损失评估研究，揭示方法仅覆盖直接即时损失的结构性偏差。","系统综述1984-2025年65篇文献，构建方法-损失类别矩阵。","所有研究仅测直接即时损失，间接、诱导及无形损失严重缺失，致损失估计系统性偏低。","可开发融合遥感与经济学估值的方法，量化长期土地退化、供应链金融与农户心理健康等新兴损失类别。","2026-10-07T23:30:25.887527Z",{"id":1714,"title":1715,"url":1716,"summary":1717,"summary_zh":1718,"content":11,"source_name":757,"source_url":1716,"published_at":1385,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1719,"score_detail":1720,"sources":1722,"tags":1724,"primary_topic":1727,"secondary_topics":1728,"classification":1730,"search_phrases":1746,"slug":1749,"view_count":69,"doi":1750,"paper":1751,"created_at":1770},4354,"Monitoring growth and ground cover of turfgrass germplasm using a UAV-mounted multispectral sensor in a ground-based system","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74076-4","Efficient selection of turfgrass germplasm with persistent green coverage requires evaluation across multiple observation periods rather than at a single time point. This study developed an integrated framework for temporal evaluation and multi-criteria selection of 526 turfgrass accessions monitored monthly from August to December 2021 during one growing season in a single experimental environment. Green cover percentage (GCP) quantified visible canopy coverage, whereas the normalized difference vegetation index (NDVI) provided complementary spectral information on canopy greenness. Four multi-month summary traits GCP area under the curve (AUC), late-season GCP, NDVI AUC, and late-season NDVI formed a common trait space examined through complementary analytical steps: K-means clustering characterized broad temporal-performance groups, Pareto screening retained non-dominated trade-offs, and an a priori equal-weight composite score ranked the resulting candidates. The three-cluster solution separated a high-performance group of 97 accessions, and the global Pareto set comprised 14 accessions. The ranking identified five promising candidates, sensitivity analysis indicated a stable core of four, whereas the fifth position depended on moderate changes in trait weights. Within the limits of a single season and environment, the results show that integrating multi-month traits, clustering, Pareto screening, composite ranking, and sensitivity analysis provides a transparent framework for prioritizing turfgrass germplasm for further evaluation.","高效选育具有持久绿色覆盖的草坪草种质，需要在多个观测时期进行评估，而非仅依据单一时间点。本研究构建了一个整合框架，用于对526份草坪草种质进行时间序列评价和多标准选择，这些种质在2021年8月至12月的一个生长季内、单一试验环境中每月监测一次。绿色覆盖百分比（GCP）量化了可见冠层覆盖度，而归一化差异植被指数（NDVI）则提供了冠层绿色度的补充光谱信息。四个多月份汇总性状——GCP曲线下面积（AUC）、后期GCP、NDVI AUC和后期NDVI——构成了一个共同性状空间，并通过互补的分析步骤加以检验：K均值聚类刻画了宽泛的时间表现分组，帕累托筛选保留了非支配权衡，先验等权综合评分对所得候选材料进行排序。三聚类方案分离出一个由97份种质组成的高表现组，全局帕累托集包含14份种质。排序确定了5份有前景的候选材料，敏感性分析表明其中4份构成稳定核心，而第5个位置取决于性状权重的适度变化。在单一生长季和环境的限制范围内，结果表明，整合多月份性状、聚类、帕累托筛选、综合排序和敏感性分析，为优先选择草坪草种质以开展进一步评价提供了一个透明的框架。",72,{"impact":416,"substance":1136,"depth":1137,"authority":345,"freshness":173,"relevant":455,"comment":1721},"将无人机多光谱遥感与聚类、帕累托筛选结合用于草坪草种质多期评价，方法新颖、数据规模可观，对智慧育种与表型监测有参考价值。",[1723],{"name":757,"url":1716},[589,871,1725,592,1726],"种质资源","草坪草",{"code":80,"slug":81,"name":82,"path":83,"parent_code":72},[1729],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1731},{"candidates":1732,"agricultural_evidence":605},[1733,1738,1742],{"code":80,"score":416,"supported":605,"title":1734,"abstract":1736,"tags":1737},[1735],"germplasm",[1735],[1725],{"code":32,"score":866,"supported":605,"title":1739,"abstract":1740,"tags":1741},[706],[],[592],{"code":16,"score":455,"supported":581,"title":1743,"abstract":1744,"tags":1745},[],[],[589],[1747,1748],"无人机 多光谱 草坪草 种质","NDVI 草坪草 绿度","无人机多光谱草坪草种质-4354","10.1038\u002Fs41598-026-74076-4",{"doi":1750,"openalex_id":1752,"authors":1753,"venue":757,"cited_by_count":424,"oa_url":1764,"card":1765,"direction":674,"ingested_from":677},"W7220416490",[1754,1757,1760,1762],{"name":1755,"orcid":1756},"E. M. B. M. Karunathilake","https:\u002F\u002Forcid.org\u002F0009-0009-8841-1480",{"name":1758,"orcid":1759},"Sheikh Mansoor","https:\u002F\u002Forcid.org\u002F0000-0003-1902-4611",{"name":1761,"orcid":11},"Yong Suk Chung",{"name":1763,"orcid":11},"Gyung Deok Han","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74076-4_reference.pdf",{"tldr":1766,"method":1767,"finding":1768,"direction":674,"opportunity":1769},"用无人机多光谱监测526份草坪草种质，结合多时相性状与多准则筛选优良品系。","无人机多光谱获取GCP和NDVI，K-means聚类、Pareto筛选与加权评分","三聚类分出97份高性能种质，Pareto集14份，最终5个候选且4个稳健。","可扩展到多环境多年份验证，并将无人机表型与基因组数据结合做育种决策。","2026-10-07T23:30:19.253986Z",{"id":1772,"title":1773,"url":1774,"summary":1775,"summary_zh":1776,"content":11,"source_name":1777,"source_url":1774,"published_at":1132,"category":580,"cover_url":11,"hotness":385,"is_selected":605,"score":582,"score_detail":1778,"sources":1780,"tags":1782,"primary_topic":1785,"secondary_topics":1786,"classification":1788,"search_phrases":1813,"slug":1816,"view_count":455,"doi":1817,"paper":1818,"created_at":1848},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data",{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":866,"relevant":455,"comment":1779},"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[1781],{"name":1777,"url":1774},[589,694,1783,609,1784],"高光谱","土壤监测",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[1787],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1789},{"candidates":1790,"agricultural_evidence":605},[1791,1795,1799,1803,1807],{"code":144,"score":345,"supported":605,"title":1792,"abstract":1793,"tags":1794},[145],[635,145],[],{"code":32,"score":689,"supported":605,"title":1796,"abstract":1797,"tags":1798},[1205],[1205],[609,1783],{"code":16,"score":626,"supported":605,"title":1800,"abstract":1801,"tags":1802},[],[697,890],[589],{"code":24,"score":626,"supported":605,"title":1804,"abstract":1805,"tags":1806},[],[591,621],[694],{"code":96,"score":632,"supported":605,"title":1808,"abstract":1809,"tags":1812},[],[1810,1811],"施肥","fertilization",[],[1814,1815],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":1817,"openalex_id":1819,"authors":1820,"venue":1777,"cited_by_count":424,"oa_url":1842,"card":1843,"direction":676,"ingested_from":677},"W7220738771",[1821,1824,1827,1830,1833,1836,1839],{"name":1822,"orcid":1823},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":1825,"orcid":1826},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":1828,"orcid":1829},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":1831,"orcid":1832},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":1834,"orcid":1835},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":1837,"orcid":1838},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":1840,"orcid":1841},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":1844,"method":1845,"finding":1846,"direction":674,"opportunity":1847},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","2026-10-07T23:30:09.147409Z",{"id":1850,"title":1851,"url":1852,"summary":1853,"summary_zh":1854,"content":11,"source_name":1855,"source_url":1852,"published_at":1385,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":253,"score_detail":1856,"sources":1859,"tags":1861,"primary_topic":1865,"secondary_topics":1866,"classification":1868,"search_phrases":1892,"slug":1895,"view_count":424,"doi":1896,"paper":1897,"created_at":1936},4313,"A semi-supervised multi-sensor remote sensing pipeline for mapping irrigation dynamics in ground-truth deficient conflict zones: a case study of the Kakhovka Dam failure","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1920951","The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On 6 June 2023, the Kakhovka Dam was destroyed. The loss of this critical agricultural and energy infrastructure has left most farmers on the East bank of the Dnipro River without irrigation in Kherson and Zaporizhzhia oblasts. This study develops a multi-sensor Earth Observation framework combining high resolution satellite imagery (Sentinel-2, Landsat) with meteorological data (CHIRPS) and water productivity data (FAO WaPOR), using a semi-supervised machine learning pipeline. Using a rigorous stratified random-sampling approach with photointerpretation, we found pre-war irrigated areas between 2019 to 2021 to be on average 187.94 ± 14.43 kHa, approximately 4% of pre-war baseline cropland and 8% of summer season cropland in Kherson and Zaporizhzhia oblasts. In 2022, these numbers contracted to 140.04 ± 17.44 kHa with the expanded occupation of these territories by Russia, seeing an additional decline in 2023 to 74.97 ± 17.84 kHa. Fields in 2023 also exhibited variability in irrigation, ranging from interrupted irrigation owing to the destruction of the Kakhovka reservoir, to those that were sufficiently irrigated with water remaining in the channels. Irrigated areas diminished further in 2024 to 15.95 ± 9.97 kHa, a ∼91% decline from pre-war averages. Based on inspection, formerly irrigated fields before 2023 were either systematically abandoned or converted to winter cropland with lower input requirements by 2024–a pattern that continued into 2025 (total irrigated area 5.76 ± 0.87 kHa). This study demonstrates the power of advanced EO workflows to precisely quantify agricultural transformation and infrastructural failure in ground-truth deficient, high-risk environments.","乌克兰持续不断的战争使国际社会关注冲突对全球农业生产和粮食安全的影响。2023年6月6日，卡霍夫卡大坝被毁。这一关键农业和能源基础设施的丧失，使第聂伯河（Dnipro River）东岸赫尔松州和扎波罗热州的大多数农民失去了灌溉条件。本研究开发了一个多传感器地球观测（Earth Observation）框架，将高分辨率卫星影像（Sentinel-2、Landsat）与气象数据（CHIRPS）和水生产力数据（FAO WaPOR）相结合，采用半监督机器学习流程。通过严格的 stratified random-sampling 方法并结合照片判读，我们发现2019年至2021年战前灌溉面积平均为187.94 ± 14.43 kHa，约占赫尔松州和扎波罗热州战前基线耕地面积的4%，以及夏季作物耕地面积的8%。2022年，随着俄罗斯对这些领土占领的扩大，上述数字缩减至140.04 ± 17.44 kHa，2023年进一步下降至74.97 ± 17.84 kHa。2023年的田块在灌溉方面也表现出差异性，从因卡霍夫卡水库被毁而灌溉中断，到因渠道中仍有存水而得到充分灌溉的情况不等。2024年，灌溉面积进一步减少至15.95 ± 9.97 kHa，较战前平均值下降约91%。根据核查，2023年之前曾受灌溉的田块到2024年要么被系统性弃耕，要么转为投入需求较低的冬季作物用地——这一模式延续至2025年（总灌溉面积为5.76 ± 0.87 kHa）。本研究表明，先进的地球观测工作流程能够在缺乏地面真值、高风险的环境中精确量化农业转型和基础设施失效。","Frontiers in Remote Sensing",{"impact":321,"substance":1857,"depth":584,"authority":689,"freshness":866,"relevant":455,"comment":1858},24,"用半监督多源遥感量化战区灌溉萎缩，方法新颖、数据扎实，对农业遥感与粮食安全研究有参考价值。",[1860],{"name":1855,"url":1852},[590,592,1862,1863,1864],"冲突农业","多源遥感","灌溉",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[1867],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1869},{"candidates":1870,"agricultural_evidence":605},[1871,1875,1879,1883,1887],{"code":152,"score":345,"supported":605,"title":1872,"abstract":1873,"tags":1874},[153],[1864,153],[],{"code":32,"score":385,"supported":605,"title":1876,"abstract":1877,"tags":1878},[607],[],[1863,592],{"code":263,"score":626,"supported":605,"title":1880,"abstract":1881,"tags":1882},[],[590,629],[590],{"code":24,"score":632,"supported":605,"title":1884,"abstract":1885,"tags":1886},[],[591,621],[],{"code":556,"score":638,"supported":605,"title":1888,"abstract":1889,"tags":1891},[],[1890],"全球农业",[],[1893,1894],"冲突农业 多源遥感 粮食安全 遥感监测","冲突农业 多源遥感","冲突农业多源遥感粮食安全遥感监测-4313","10.3389\u002Ffrsen.2026.1920951",{"doi":1896,"openalex_id":1898,"authors":1899,"venue":1855,"cited_by_count":424,"oa_url":1930,"card":1931,"direction":674,"ingested_from":677},"W7220438608",[1900,1903,1906,1909,1912,1915,1918,1920,1922,1924,1927],{"name":1901,"orcid":1902},"Sheila Baber","https:\u002F\u002Forcid.org\u002F0000-0002-5596-8970",{"name":1904,"orcid":1905},"Sergii V. Skakun","https:\u002F\u002Forcid.org\u002F0000-0002-9039-0174",{"name":1907,"orcid":1908},"Yuval Sadeh","https:\u002F\u002Forcid.org\u002F0000-0002-4346-0432",{"name":1910,"orcid":1911},"Леонід Леонідович Шуміло","https:\u002F\u002Forcid.org\u002F0000-0002-7395-7933",{"name":1913,"orcid":1914},"Oleksandra Oliinyk","https:\u002F\u002Forcid.org\u002F0000-0002-2392-3938",{"name":1916,"orcid":1917},"Mehdi Hosseini","https:\u002F\u002Forcid.org\u002F0000-0003-3242-613X",{"name":1919,"orcid":11},"Josef Wagner",{"name":1921,"orcid":11},"Abdul Qadir Khan",{"name":1923,"orcid":11},"Shabarinath Nair",{"name":1925,"orcid":1926},"Abhishek Kotcharlakota","https:\u002F\u002Forcid.org\u002F0009-0001-1832-4631",{"name":1928,"orcid":1929},"Inbal Becker‐Reshef","https:\u002F\u002Forcid.org\u002F0000-0002-2160-5151","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1920951\u002Fpdf",{"tldr":1932,"method":1933,"finding":1934,"direction":674,"opportunity":1935},"用半监督多源遥感监测卡霍夫卡大坝被毁后灌溉面积从战前18.8万公顷降至2025年0.58万公顷。","Sentinel-2、Landsat、CHIRPS与FAO WaPOR数据结合半","灌溉面积由2019-2021年均187.94 kHa降至2024年15.95 kHa，降幅约91%，","可将该半监督多源框架迁移至其他缺地面真值冲突区，并耦合作物产量与粮食安全评估。","2026-10-06T23:30:20.495894Z",{"technologies":1938,"keywords":1974},[1939,1940,1942,1944,1945,1946,1947,1949,1950,1951,1953,1954,1956,1958,1960,1961,1963,1964,1965,1967,1968,1970,1972],{"name":609,"count":157},{"name":592,"count":1941},109,{"name":694,"count":1943},86,{"name":695,"count":197},{"name":591,"count":1857},{"name":901,"count":1136},{"name":1948,"count":689},"作物表型",{"name":697,"count":689},{"name":620,"count":173},{"name":1952,"count":626},"无人机遥感",{"name":1783,"count":408},{"name":1955,"count":408},"高光谱遥感",{"name":1957,"count":400},"多光谱成像",{"name":1959,"count":638},"卫星遥感",{"name":1143,"count":69},{"name":1962,"count":69},"农业物联网",{"name":1863,"count":69},{"name":696,"count":69},{"name":1966,"count":69},"无人机多光谱",{"name":1317,"count":455},{"name":1969,"count":455},"农产品溯源",{"name":1971,"count":455},"目标检测",{"name":1973,"count":455},"边缘计算",[1975,1977,1979,1980,1982,1983,1984,1986,1988,1989,1990,1991],{"name":589,"count":1976},160,{"name":1081,"count":1978},31,{"name":1080,"count":345},{"name":1981,"count":345},"产量预测",{"name":210,"count":689},{"name":1142,"count":524},{"name":1985,"count":385},"植被指数",{"name":1987,"count":385},"水资源管理",{"name":1471,"count":866},{"name":1590,"count":866},{"name":218,"count":866},{"name":590,"count":866}]