[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1600":3,"related-1600":105},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":15,"sources":16,"tags":18,"primary_topic":8,"secondary_topics":24,"search_phrases":25,"slug":28,"view_count":29,"doi":30,"paper":31,"created_at":66,"content":8,"score_detail":67,"classification":74},1600,"Integrating proximal soil sensing and Sentinel-1\u002F2 remote sensing for management zone delineation and monitoring of variable-rate nitrogen fertilization in winter barley","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102542","Integrating proximal soil sensing and Sentinel-1\u002F2 remote sensing for management zone delineation and monitoring of variable-rate nitrogen fertilization in winter barley。Smart Agricultural Technology",null,"Smart Agricultural Technology","2026-09-01T00:00:00Z","论文",10,false,true,66,[17],{"name":9,"url":6},[19,20,21,22,23],"智慧农业","变量施肥","精准农业","遥感","冬大麦",[],[26,27],"变量施肥 智慧农业 精准农业 冬大麦","变量施肥 智慧农业","变量施肥智慧农业精准农业冬大麦-1600",2,"10.1016\u002Fj.atech.2026.102542",{"doi":30,"openalex_id":32,"authors":33,"venue":9,"cited_by_count":64,"oa_url":6,"card":8,"direction":8,"ingested_from":65},"W7207736997",[34,36,38,41,44,46,48,50,52,54,56,58,61],{"name":35,"orcid":8},"Nazerke Amangeldy",{"name":37,"orcid":8},"Eduardo Garcia-Braga",{"name":39,"orcid":40},"Yasmina Chourak","https:\u002F\u002Forcid.org\u002F0000-0001-7634-3497",{"name":42,"orcid":43},"Gerard Portal","https:\u002F\u002Forcid.org\u002F0000-0003-0797-6711",{"name":45,"orcid":8},"Hongzhen Luo",{"name":47,"orcid":8},"Isi Bardají",{"name":49,"orcid":8},"Mercè Vall-llossera",{"name":51,"orcid":8},"Rosa Vilaplana Ventura",{"name":53,"orcid":8},"Antonios Morellos",{"name":55,"orcid":8},"Manuel Vázquez-Arellano",{"name":57,"orcid":8},"Erik Meers",{"name":59,"orcid":60},"Estefanía Blanch","https:\u002F\u002Forcid.org\u002F0000-0002-8438-2776",{"name":62,"orcid":63},"Abdul Mounem Mouazen","https:\u002F\u002Forcid.org\u002F0000-0002-0354-0067",0,"openalex","2026-09-04T23:30:04.649483Z",{"impact":68,"substance":69,"depth":70,"authority":68,"freshness":71,"relevant":72,"comment":73},12,20,18,4,1,"研究结合近地传感与卫星遥感，为冬大麦管理分区和变量施氮提供新方法，具实践价值。",{"status":75,"locked":13,"version":76,"method":77,"evidence":78},"needs_review","2026-10-22.2","rules",{"candidates":79,"agricultural_evidence":14,"reason":104},[80,86,92,99],{"code":81,"score":68,"supported":14,"title":82,"abstract":84,"tags":85},"F3",[83],"fertilization",[83],[20],{"code":87,"score":68,"supported":14,"title":88,"abstract":90,"tags":91},"I3",[89],"remote sensing",[89],[22],{"code":93,"score":94,"supported":14,"title":95,"abstract":97,"tags":98},"H2",11,[96],"soil",[96],[],{"code":100,"score":29,"supported":13,"title":101,"abstract":102,"tags":103},"I1",[],[],[19,21],"多个产业证据相当，需要确认主归属",{"total":106,"page":72,"page_size":106,"items":107},6,[108,158,194,243,286,320],{"id":109,"title":110,"url":111,"summary":112,"summary_zh":113,"source_name":114,"source_url":111,"published_at":115,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":116,"sources":117,"tags":119,"primary_topic":121,"secondary_topics":127,"search_phrases":134,"slug":137,"view_count":138,"doi":139,"paper":140,"created_at":157},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",83,[118],{"name":114,"url":111},[120,19,20,21,22],"数字农业",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[128],{"code":129,"slug":130,"name":131,"path":132,"parent_code":133},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[135,136],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693",3,"10.5281\u002Fzenodo.23282433",{"doi":139,"openalex_id":141,"authors":142,"venue":114,"cited_by_count":64,"oa_url":111,"card":150,"direction":156,"ingested_from":65},"W7221365319",[143,145,147],{"name":144,"orcid":8},"Zoltán Szenek",{"name":146,"orcid":8},"Donát Szám",{"name":148,"orcid":149},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",{"tldr":151,"method":152,"finding":153,"direction":154,"opportunity":155},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","农业遥感与作物表型","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","数字乡村与农业信息化","2026-10-11T23:30:36.790037Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"source_name":164,"source_url":161,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":166,"sources":167,"tags":169,"primary_topic":172,"secondary_topics":173,"search_phrases":174,"slug":177,"view_count":64,"doi":178,"paper":179,"created_at":193},5611,"RGB UAV Imagery for Crop Monitoring and Decision Support in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100448","Digitalization and data-driven decision-making are transforming modern agriculture, where rapid and high-resolution assessment of crop condition is essential for optimizing field interventions. Multispectral approaches such as satellite-derived normalized difference vegetation index are widely used; however, their spatial resolution and temporal availability may limit the detection of fine-scale variability within agricultural fields. This study investigates the capability of unmanned aerial vehicle (UAV)-acquired red, green, and blue (RGB) imagery to provide relevant information regarding crop condition. Two autumn crops, wheat and rapeseed, located in Olt County, Romania, were analyzed. The acquired images were processed photogrammetrically to generate orthomosaics, followed by the extraction of vegetation indices derived from the visible spectrum, namely Excess Green Index and Visible Atmospherically Resistant Index. Both indices provided meaningful information on vegetation condition, with the latter exhibiting enhanced sensitivity to intra-field variability. The UAV-derived Excess Green Index and Visible Atmospherically Resistant Index maps revealed spatial variability that was not equally distinguishable in the satellite Normalized Difference Vegetation Index data, particularly for the wheat crop. Individually, the two RGB vegetation indices classified approximately 16% of each analyzed field as vulnerable. These vulnerable regions represent relative, image-based spectral anomalies rather than independently validated indicators of physiological crop stress, nutrient deficiency, or yield loss. Following rule-based fusion and the removal of isolated detections, 21.55% of the wheat field and 19.75% of the rapeseed field were identified as spatially coherent vulnerable areas. The resulting maps were subsequently converted into machinery-oriented candidate intervention zones, supporting an illustrative simulation of how spatially differentiated field information could be integrated into conventional agricultural equipment. These findings demonstrate that UAV-based RGB imagery can provide spatially explicit information for crop monitoring and can support the identification of candidate areas for subsequent field verification and precision-agriculture decision-making.","数字化与数据驱动的决策正在改变现代农业，其中对作物状况进行快速、高分辨率评估对于优化田间干预措施至关重要。卫星衍生的归一化差异植被指数（NDVI）等多光谱方法已被广泛使用；然而，其空间分辨率和时间可获得性可能限制了对农田内部细尺度变异的检测。本研究探讨了无人机（UAV）获取的红、绿、蓝（RGB）影像在提供作物状况相关信息方面的能力。研究分析了位于罗马尼亚奥尔特县的两种秋季作物——小麦和油菜。对获取的影像进行摄影测量处理以生成正射镶嵌图，随后提取基于可见光谱的植被指数，即超绿指数（Excess Green Index）和可见光大气阻抗指数（Visible Atmospherically Resistant Index）。两种指数均提供了有关植被状况的有意义信息，其中后者对田内变异表现出更高的敏感性。无人机衍生的超绿指数和可见光大气阻抗指数图揭示了卫星归一化差异植被指数数据中未能同等区分出的空间变异，尤其对小麦作物而言。单独来看，这两种RGB植被指数将每个分析田块约16%的区域归类为脆弱区。这些脆弱区域代表的是基于影像的相对光谱异常，而非经过独立验证的作物生理胁迫、养分缺乏或产量损失指标。经过基于规则的融合并去除孤立检测后，小麦田的21.55%和油菜田的19.75%被识别为空间连贯的脆弱区域。随后将所得地图转换为面向机械的候选干预区，支持了一项示意性模拟，展示如何将空间差异化的田间信息整合到常规农业设备中。这些发现表明，基于无人机的RGB影像可为作物监测提供空间明确的信息，并能够支持识别供后续田间验证和精准农业决策的候选区域。","AgriEngineering","2026-10-21T00:00:00Z",68,[168],{"name":164,"url":161},[19,170,21,22,171],"无人机","作物监测",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},[],[175,176],"RGB无人机 作物监测","Olt 小麦 油菜","RGB无人机作物监测-5611","10.3390\u002Fagriengineering8100448",{"doi":178,"openalex_id":180,"authors":181,"venue":164,"cited_by_count":64,"oa_url":161,"card":188,"direction":156,"ingested_from":65},"W7223174201",[182,185],{"name":183,"orcid":184},"Robert Ciobanu","https:\u002F\u002Forcid.org\u002F0000-0001-7518-974X",{"name":186,"orcid":187},"Emilia Georgiana Prisăcariu","https:\u002F\u002Forcid.org\u002F0009-0005-2437-7288",{"tldr":189,"method":190,"finding":191,"direction":154,"opportunity":192},"用无人机RGB影像提取植被指数监测小麦和油菜长势并生成精准干预区。","无人机RGB影像摄影测量生成正射图，计算ExG与VARI指数并规则融合。","RGB指数可识别卫星NDVI难分辨的田间细微变异，约20%田块为脆弱区。","可结合地面验证与多源遥感，将RGB脆弱区图转化为可验证的胁迫诊断与变量作业决策。","2026-10-22T23:30:52.745269Z",{"id":195,"title":196,"url":197,"summary":198,"summary_zh":199,"source_name":200,"source_url":197,"published_at":201,"category":11,"cover_url":8,"hotness":12,"is_selected":14,"embedding_ready":14,"score":202,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"search_phrases":210,"slug":213,"view_count":71,"doi":214,"paper":215,"created_at":242},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,[204],{"name":200,"url":197},[19,206,21,22,207],"农业人工智能","全球评估",{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},[],[211,212],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":214,"openalex_id":216,"authors":217,"venue":200,"cited_by_count":64,"oa_url":197,"card":236,"direction":240,"ingested_from":65},"W7222974188",[218,221,224,227,230,233],{"name":219,"orcid":220},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":222,"orcid":223},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":225,"orcid":226},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":228,"orcid":229},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":231,"orcid":232},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":234,"orcid":235},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","智慧农业 \u002F 农业物联网","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"source_name":249,"source_url":246,"published_at":250,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":251,"sources":252,"tags":254,"primary_topic":257,"secondary_topics":261,"search_phrases":271,"slug":274,"view_count":29,"doi":8,"paper":275,"created_at":285},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,[253],{"name":249,"url":246},[19,21,22,255,256],"盐碱地治理","土壤监测",{"code":87,"slug":258,"name":259,"path":260,"parent_code":126},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[262,267],{"code":263,"slug":264,"name":265,"path":266,"parent_code":126},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":93,"slug":96,"name":268,"path":269,"parent_code":270},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[272,273],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":8,"openalex_id":276,"authors":277,"venue":249,"cited_by_count":64,"oa_url":246,"card":280,"direction":154,"ingested_from":65},"W7221843179",[278],{"name":279,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":281,"method":282,"finding":283,"direction":154,"opportunity":284},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"source_name":292,"source_url":289,"published_at":250,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":293,"sources":294,"tags":296,"primary_topic":299,"secondary_topics":300,"search_phrases":302,"slug":305,"view_count":72,"doi":306,"paper":307,"created_at":319},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience",72,[295],{"name":292,"url":289},[19,21,22,297,298],"土壤健康","覆盖作物",{"code":93,"slug":96,"name":268,"path":269,"parent_code":270},[301],{"code":87,"slug":258,"name":259,"path":260,"parent_code":126},[303,304],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":306,"openalex_id":308,"authors":309,"venue":292,"cited_by_count":64,"oa_url":8,"card":314,"direction":154,"ingested_from":65},"W7221669942",[310,312],{"name":311,"orcid":8},"Ali Mirzakhani Nafchi",{"name":313,"orcid":8},"Salman Mirzaee",{"tldr":315,"method":316,"finding":317,"direction":154,"opportunity":318},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":321,"title":322,"url":323,"summary":324,"summary_zh":325,"source_name":326,"source_url":323,"published_at":327,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"embedding_ready":14,"score":328,"sources":329,"tags":331,"primary_topic":333,"secondary_topics":337,"search_phrases":338,"slug":341,"view_count":72,"doi":342,"paper":343,"created_at":359},4825,"Multi-Source Remote Sensing Fusion and Deep Learning for Intelligent Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206434","The fusion of multi-source remote sensing data and deep learning technology has been increasingly applied in precision agriculture. This review focuses on the perception–decision–execution framework and summarizes the current application status, technical routes, and development trends of multi-source remote sensing data fusion and deep learning in precision agriculture. It examines the complementary advantages of multimodal data such as optical, thermal infrared (TIR), Light Detection and Ranging (LiDAR), and synthetic aperture radar (SAR) in characterizing crop physiological traits and structural characteristics. At the algorithmic level, this review analyzes the evolution of deep learning architectures from single convolutional neural networks (CNNs) to spatiotemporal fusion networks (STFNs), Transformer-based architectures, and emerging state-space models, with emphasis on their capabilities for heterogeneous feature extraction and cross-modal information integration. This review discusses the transformation mechanism from intelligent perception to executable decision-making and explores the application effectiveness of prescription maps and variable-rate task technology in improving resource utilization. This review proposes forward-looking views on core bottlenecks such as model generalization, physical interpretability, and edge computing. This review not only provides a theoretical analysis of precision agriculture but also provides academic recommendations for the development of intelligent agricultural equipment in the future.","多源遥感数据与深度学习技术的融合已在精准农业中得到日益广泛的应用。本文以“感知—决策—执行”框架为主线，综述了多源遥感数据融合与深度学习在精准农业中的应用现状、技术路线与发展趋势。文章考察了光学、热红外（TIR）、激光雷达（LiDAR）和合成孔径雷达（SAR）等多模态数据在表征作物生理性状与结构特征方面的互补优势。在算法层面，本文分析了深度学习架构从单一卷积神经网络（CNN）向时空融合网络（STFN）、基于Transformer的架构以及新兴状态空间模型的演进，重点探讨了其在异构特征提取与跨模态信息整合方面的能力。本文讨论了从智能感知到可执行决策的转化机制，并探究了处方图与变量作业技术在提升资源利用率方面的应用效果。针对模型泛化能力、物理可解释性与边缘计算等核心瓶颈，本文提出了前瞻性观点。本文不仅为精准农业提供了理论分析，也为未来智能农业装备的发展提供了学术建议。","Sensors","2026-10-12T00:00:00Z",78,[330],{"name":326,"url":323},[19,206,21,22,332],"多源数据融合",{"code":126,"slug":334,"name":335,"path":336,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],[339,340],"多源遥感 深度学习 精准农业","Transformer 遥感 作物表型","多源遥感深度学习精准农业-4825","10.3390\u002Fs26206434",{"doi":342,"openalex_id":344,"authors":345,"venue":326,"cited_by_count":64,"oa_url":323,"card":354,"direction":154,"ingested_from":65},"W7221565940",[346,349,352],{"name":347,"orcid":348},"Zhen Ma","https:\u002F\u002Forcid.org\u002F0000-0003-4377-9651",{"name":350,"orcid":351},"Xiyuan Chen","https:\u002F\u002Forcid.org\u002F0000-0002-1770-8529",{"name":353,"orcid":8},"Cundeng Wang",{"tldr":355,"method":356,"finding":357,"direction":154,"opportunity":358},"综述多源遥感融合与深度学习在精准农业感知-决策-执行框架中的应用与趋势。","综述光学、热红外、LiDAR、SAR融合及CNN到Transformer、状态空","多模态融合与先进深度学习架构可提升作物感知与处方图决策，但泛化性、可解释性和边缘计算仍是瓶颈。","可探索轻量化状态空间模型与物理约束融合，提升跨区域泛化及边缘端实时决策能力。","2026-10-13T23:30:24.008972Z"]