[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3500":3,"related-3500":57},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":56},3500,"Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10333-026-01102-6","Remote sensing-driven soil fertility modelling and summer rice suitability mapping in a floodplain agroecosystem of Assam。Paddy and Water Environment",null,"Paddy and Water Environment","2026-09-24T00:00:00Z","论文",10,false,67,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,13,8,1,"核心期刊论文，遥感驱动土壤肥力建模与水稻适宜性制图，方法有参考价值但属区域案例，未达每日精选门槛。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业信息化","遥感","土壤肥力","水稻种植",[32,33],"Assam 洪泛平原 水稻 遥感","土壤肥力 遥感 建模","Assam洪泛平原水稻遥感-3500",0,"10.1007\u002Fs10333-026-01102-6",{"doi":36,"openalex_id":38,"authors":39,"venue":9,"cited_by_count":35,"oa_url":8,"card":8,"direction":54,"ingested_from":55},"W7214202229",[40,42,45,47,50,52],{"name":41,"orcid":8},"Pompy Deka",{"name":43,"orcid":44},"Jwngsar Moshahary","https:\u002F\u002Forcid.org\u002F0000-0002-3409-6691",{"name":46,"orcid":8},"Mrinal Choudhury",{"name":48,"orcid":49},"Perves Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2114-1043",{"name":51,"orcid":8},"Britan Rahman",{"name":53,"orcid":8},"Ranjit Sarma","农业遥感与作物表型","openalex","2026-09-25T23:30:30.923946Z",{"total":58,"page":21,"page_size":58,"items":59},6,[60,102,143,185,228,277],{"id":61,"title":62,"url":63,"summary":64,"summary_zh":65,"content":8,"source_name":66,"source_url":63,"published_at":67,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":68,"score_detail":69,"sources":73,"tags":75,"search_phrases":78,"slug":81,"view_count":35,"doi":82,"paper":83,"created_at":101},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems","2026-09-19T00:00:00Z",70,{"impact":16,"substance":70,"depth":71,"authority":19,"freshness":20,"relevant":21,"comment":72},20,17,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[74],{"name":66,"url":63},[26,76,27,28,77],"农业遥感","土地利用",[79,80],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":82,"openalex_id":84,"authors":85,"venue":66,"cited_by_count":35,"oa_url":94,"card":95,"direction":100,"ingested_from":55},"W7213644047",[86,88,90,92],{"name":87,"orcid":8},"Sreerama Naik Naik S R",{"name":89,"orcid":8},"T K Prasad",{"name":91,"orcid":8},"Feba Jose Jasmine",{"name":93,"orcid":8},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":96,"method":97,"finding":98,"direction":54,"opportunity":99},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":8,"source_name":108,"source_url":105,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":109,"score_detail":110,"sources":114,"tags":116,"search_phrases":120,"slug":123,"view_count":35,"doi":124,"paper":125,"created_at":142},3515,"Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13198-026-03435-1","Soil mapping and fertilizer optimization for precision agriculture using artificial intelligence。International Journal of Systems Assurance Engineering and Management","基于人工智能的精准农业土壤制图与肥料优化。《国际系统保障工程与管理杂志》","International Journal of Systems Assurance Engineering and Management",62,{"impact":16,"substance":111,"depth":112,"authority":19,"freshness":20,"relevant":21,"comment":113},14,15,"论文探讨AI用于土壤制图与施肥优化，属智慧农业细分方向，但摘要信息有限、影响面偏窄，暂不建议进入每日精选。",[115],{"name":108,"url":105},[26,117,118,28,119],"农业人工智能","精准施肥","土壤制图",[121,122],"土壤制图 人工智能 精准施肥","精准农业 肥料优化 AI","土壤制图人工智能精准施肥-3515","10.1007\u002Fs13198-026-03435-1",{"doi":124,"openalex_id":126,"authors":127,"venue":108,"cited_by_count":35,"oa_url":8,"card":136,"direction":140,"ingested_from":55},"W7214144821",[128,131,133],{"name":129,"orcid":130},"Neetu Mittal","https:\u002F\u002Forcid.org\u002F0000-0002-2012-0523",{"name":129,"orcid":132},"https:\u002F\u002Forcid.org\u002F0000-0001-6923-0013",{"name":134,"orcid":135},"Pradeepta Kumar Sarangi","https:\u002F\u002Forcid.org\u002F0000-0003-3827-6208",{"tldr":137,"method":138,"finding":139,"direction":140,"opportunity":141},"利用人工智能进行土壤制图和肥料优化，以支持精准农业。","人工智能方法，用于土壤制图与肥料优化。","AI可提升土壤制图与肥料优化的精准性，促进精准农业。","农业人工智能与决策模型","可探索多源数据融合与实时决策模型，提升肥料推荐的自适应性和可解释性。","2026-09-25T23:30:49.869002Z",{"id":144,"title":145,"url":146,"summary":147,"summary_zh":148,"content":8,"source_name":149,"source_url":146,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":150,"score_detail":151,"sources":155,"tags":157,"search_phrases":161,"slug":164,"view_count":35,"doi":165,"paper":166,"created_at":184},3501,"A Dual-Phenological-Characteristic Weighting Method to Reconcile Time Discrepancies in Soybean Phenology Estimation from MODIS NDVI Time Series","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193300","Accurate large-scale monitoring of crop phenology is essential for optimizing agricultural management. Remote sensing has been widely used for estimating crop phenological stages, yet time discrepancies often exist between remotely sensed phenological metrics and ground-observed growth stages. Moreover, phenological parameters derived from different characterization models exhibit varying degrees of deviation from field observations. The primary goal of this study was to develop a novel method that fully exploits the deviation patterns of diverse phenological parameters to enhance the accuracy of soybean phenology retrieval. To this end, we extracted 11 phenological parameters for six key growth stages—emerged, blooming, pod-setting, turning yellow, dropping leaf, and harvest—of soybean across 16 U.S. states using MODIS NDVI (normalized difference vegetation index) time-series data from 2000 to 2020, employing GU-, curvature-, and derivative-based phenological modeling methods. The study design centered on proposing a dual-phenological-characteristic weighting (DPCW) method that leverages the deviation features of different phenological parameters relative to ground-observed growth stages, generating composite phenological characteristics by pairing two distinct parameters. The key innovation of this paper is the use of dual-feature weighting to improve the correspondence between satellite-derived phenometrics and field observations, offering an alternative to conventional phenological estimation. The results demonstrated that the optimal DPCW-based combinations for the six growth stages were SOS (start of season) and GREEN, SOS and POS (peak of season), MATURITY and POS, EOS (end of season) and SENES (senescence), RD (recession date) and DD (downturn date), and EOS and DORM (dormancy), respectively. The coefficient of determination (R2) between the retrieved transition dates and ground observations exceeded 0.65 for most stages, with the emerged stage improving to 0.47 from 0.052 and 0.357 of the unadjusted and offset-adjusted benchmarks. The average root mean square error (RMSE) was less than 5 days in most cases, representing a reduction of over 40%, with the most substantial improvement at the turning yellow stage, where RMSE dropped from 12.8 days to 2.8 days. A strength of this study lies in its multi-state, multi-decade validation, demonstrating the robustness and temporal consistency of the DPCW method within the major U.S. soybean-growing region. However, a limitation is that the method’s performance may vary with different satellite sensors or crop types, warranting further investigation. The proposed approach is expected to enhance the accuracy of remote sensing-based crop phenology monitoring and offers an effective alternative for calibrating remotely sensed phenological parameters.","准确的大尺度作物物候监测对于优化农业管理至关重要。遥感已被广泛用于估算作物物候阶段，但遥感物候指标与地面观测生育阶段之间常存在时间差异。此外，不同特征化模型衍生的物候参数与田间观测之间存在不同程度的偏差。本研究的主要目标是开发一种新方法，充分利用多种物候参数的偏差模式，以提高大豆物候反演精度。为此，我们利用2000—2020年MODIS NDVI（归一化差异植被指数）时间序列数据，采用基于GU、曲率和导数的方法提取了美国16个州大豆六个关键生育阶段——出苗、开花、结荚、黄化、落叶和收获——的11个物候参数。研究设计的核心是提出一种双物候特征加权（dual-phenological-characteristic weighting，DPCW）方法，该方法利用不同物候参数相对于地面观测生育阶段的偏差特征，通过配对两个不同参数生成复合物候特征。本文的关键创新在于利用双特征加权提高卫星衍生物候指标与田间观测之间的对应关系，为传统物候估算提供了一种替代方案。结果表明，六个生育阶段基于DPCW的最优组合分别为SOS（生长季开始）与GREEN、SOS与POS（生长季峰值）、MATURITY与POS、EOS（生长季结束）与SENES（衰老）、RD（衰退日期）与DD（下降日期）以及EOS与DORM（休眠）。反演得到的转换日期与地面观测之间的决定系数（R²）在大多数阶段超过0.65，其中出苗阶段从基准的0.052和偏移调整后的0.357提高至0.47。大多数情况下平均均方根误差（RMSE）小于5天，降幅超过40%，其中黄化阶段改善最为显著，RMSE从12.8天降至2.8天。本研究的一个优势在于其多州、多年代际验证，证明了DPCW方法在美国主要大豆种植区内的稳健性和时间一致性。","Remote Sensing",81,{"impact":17,"substance":152,"depth":17,"authority":111,"freshness":153,"relevant":21,"comment":154},22,9,"提出双物候特征加权方法，用MODIS NDVI长时序数据校正大豆物候遥感估算偏差，方法新颖、验证扎实，对农情遥感监测有参考价值。",[156],{"name":149,"url":146},[26,158,159,28,160],"大豆","农情监测","作物表型",[162,163],"MODIS NDVI 大豆 物候","美国大豆 遥感 物候监测","MODISNDVI大豆物候-3501","10.3390\u002Frs18193300",{"doi":165,"openalex_id":167,"authors":168,"venue":149,"cited_by_count":35,"oa_url":146,"card":179,"direction":54,"ingested_from":55},"W7214203102",[169,171,174,177],{"name":170,"orcid":8},"Qiuxiang Yi",{"name":172,"orcid":173},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":175,"orcid":176},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":178,"orcid":8},"Qinyan Zhu",{"tldr":180,"method":181,"finding":182,"direction":54,"opportunity":183},"提出双物候特征加权法，校正MODIS NDVI大豆物候估计与地面观测的时间偏差。","用MODIS NDVI 2000-2020数据，结合GU、曲率、导数三类物候模型","多数生育期R²超0.65，RMSE多小于5天，降幅超40%，转黄期RMSE从12.8天降至2.8天。","可将该加权校正思路迁移到其他作物与多源遥感数据，并探索自适应权重与深度学习融合的物候反演。","2026-09-25T23:30:31.075499Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":8,"source_name":191,"source_url":188,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":192,"score_detail":193,"sources":195,"tags":197,"search_phrases":200,"slug":203,"view_count":35,"doi":204,"paper":205,"created_at":227},3495,"Remote Sensing and GIS in Modern Drought Assessment: Bridging Conventional Methods and Emerging Technologies","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123","Drought is a complex and recurring hydroclimatic hazard that affects agricultural production, water resources, ecosystems and socioeconomic development. Effective drought assessment requires approaches capable of capturing its spatial and temporal variability and its multiple dimensions. This review examines the evolution of drought assessment from conventional drought indices to integrated approaches based on remote sensing and Geographic Information Systems (GIS), with an emphasis on their applications, strengths, limitations and emerging developments. Conventional indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), Reconnaissance Drought Index (RDI) and Percent of Normal Precipitation Index (PNPI), remain widely used because of their established methodologies and long-term applicability. However, their dependence on meteorological observations can limit spatial characterisation and the representation of vegetation, soil moisture and other land-surface responses. Remote sensing provides spatially extensive and repeated observations of vegetation condition, land surface temperature, soil moisture, evapotranspiration and water-related conditions, enabling the development of satellite-derived drought indicators and indices. GIS further facilitates the integration, spatial analysis, visualisation, and mapping of drought-related information from multiple sources. The review also discusses hybrid approaches that combine climate-based indices with satellite-derived indicators, as well as drought monitoring platforms and multi-source assessment frameworks. Despite substantial advances, challenges remain regarding cloud contamination, differences in spatial and temporal resolution, data continuity, ground-based validation and uncertainty associated with multi-source datasets. Emerging machine learning, deep learning and artificial intelligence approaches offer opportunities for integrating heterogeneous datasets and improving drought characterisation and early warning. Overall, the integration of conventional observations, remote sensing, GIS and advanced analytical approaches provides a promising framework for more comprehensive drought monitoring and risk assessment under increasing climate variability and change.","干旱是一种复杂且反复出现的水文气候灾害，影响农业生产、水资源、生态系统和社会经济发展。有效的干旱评估需要能够捕捉其时空变异性和多维特征的方法。本文综述了干旱评估从传统干旱指数到基于遥感与地理信息系统（GIS）的综合方法的演变，重点探讨其应用、优势、局限性和新兴发展。传统指数，包括标准化降水指数（SPI）、标准化降水蒸散指数（SPEI）、帕尔默干旱强度指数（PDSI）、侦察干旱指数（RDI）和降水距平百分率指数（PNPI），因其方法成熟且具有长期适用性而仍被广泛使用。然而，这些指数对气象观测的依赖可能限制其空间表征能力以及对植被、土壤水分和其他陆面响应的刻画。遥感提供了对植被状况、地表温度、土壤水分、蒸散量及与水相关状况的大范围重复观测，使得卫星衍生的干旱指标和指数得以发展。GIS进一步促进了多来源干旱相关信息的整合、空间分析、可视化和制图。本文还讨论了将基于气候的指数与卫星衍生指标相结合的混合方法，以及干旱监测平台和多源评估框架。尽管取得了实质性进展，但在云污染、时空分辨率差异、数据连续性、地面验证以及多源数据集相关的不确定性方面仍存在挑战。新兴的机器学习、深度学习和人工智能方法为整合异质数据集、改进干旱表征和预警提供了机遇。总体而言，在气候变异性和变化日益加剧的背景下，传统观测、遥感、GIS和先进分析方法的整合为更全面的干旱监测和风险评估提供了一个有前景的框架。","Journal of Geography Environment and Earth Science International",66,{"impact":16,"substance":17,"depth":18,"authority":16,"freshness":20,"relevant":21,"comment":194},"综述系统梳理遥感与GIS在干旱评估中的应用演进，方法学价值明确，但属综述类论文、非国内落地事件，影响力有限。",[196],{"name":191,"url":188},[26,117,28,198,199],"GIS","干旱监测",[201,202],"遥感 GIS 干旱评估","卫星遥感 干旱指数","遥感GIS干旱评估-3495","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91123",{"doi":204,"openalex_id":206,"authors":207,"venue":191,"cited_by_count":35,"oa_url":188,"card":222,"direction":54,"ingested_from":55},"W7214156857",[208,210,212,214,216,218,220],{"name":209,"orcid":8},"V. Dhanalakshmi",{"name":211,"orcid":8},"N. Manikandan",{"name":213,"orcid":8},"V. S. Jinsy",{"name":215,"orcid":8},"K. V. Sumesh",{"name":217,"orcid":8},"P. Nideesh",{"name":219,"orcid":8},"P. S. Manju",{"name":221,"orcid":8},"N. Gopika",{"tldr":223,"method":224,"finding":225,"direction":54,"opportunity":226},"综述了从传统干旱指数到遥感、GIS及AI集成的现代干旱评估方法演进。","文献综述，对比SPI、SPEI等传统指数与遥感、GIS及混合方法。","遥感与GIS弥补传统指数空间局限，但云污染、分辨率差异和验证仍是挑战。","可探索多源遥感与机器学习融合的干旱早期预警，重点解决数据不确定性与地面验证。","2026-09-25T23:30:30.576065Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":149,"source_url":231,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":234,"score_detail":235,"sources":237,"tags":239,"search_phrases":243,"slug":246,"view_count":35,"doi":247,"paper":248,"created_at":276},3494,"Rice Cropping Pattern Mapping Using GEE-Based Sentinel-1\u002F2 Time-Series Imagery and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193302","Accurate mapping of rice cropping patterns is fundamental to sustainable agricultural management and regional food security assessment. In this study, we developed a high-precision mapping framework for major rice cropping patterns in the Yangtze River Delta region, China, using the Google Earth Engine (GEE) cloud platform. A multi-temporal and multi-source feature set was constructed by integrating Sentinel-1 radar backscatter (VV\u002FVH polarization), Sentinel-2 optical indices, including the normalized difference vegetation index (NDVI) and land surface water index (LSWI), and topographic factors (DEM and slope), to classify three predominant cropping systems, namely, wheat–rice rotation, double rice, and rapeseed–rice rotation. A total of 548 field-surveyed sample points collected during the 2024–2025 growing season were used for model training and validation. Three classical classifiers—Random Forest (RF), A Gradient Boosting Tree (GBTREE), and a Support Vector Machine (SVM)—were systematically compared. The ablation experiment demonstrated that the fusion of Sentinel-1 and Sentinel-2 outperformed both the Sentinel-1-only and Sentinel-2-only configurations across all three classifiers. Among them, the GBTREE achieved the highest overall accuracy (93.8%), Kappa coefficient (0.87), and macro-average F1 score (88.3%) in this specific experiment. Notably, it also performed best on the more challenging double-rice class. The SHapley Additive exPlanations (SHAP)-based feature importance analysis revealed that multi-temporal NDVI phenological features were the primary drivers of classification accuracy, while radar backscatter and water indices provided essential complementary information, and topographic factors served as spatial constraints at the regional scale. The spatial distribution derived from the GBTREE classification exhibited clear patterns: wheat–rice rotation dominated the northern plains (northern Jiangsu, northern Anhui, and the Hangjiahu Plain); double rice was concentrated in the southern Zhejiang hills and scattered valley plains; and rapeseed–rice rotation showed a scattered, mosaic distribution. Overall, this study demonstrates that integrating multi-source remote sensing data on the GEE platform with the GBTREE classifier enables effective and scalable high-precision mapping of rice cropping patterns in complex agricultural landscapes. This approach provides a reliable technical foundation for regional agricultural structure analysis, crop rotation assessment, and sustainable agricultural monitoring.","准确绘制水稻种植模式图是实现可持续农业管理和区域粮食安全评估的基础。本研究基于Google Earth Engine（GEE）云平台，构建了长江三角洲地区主要水稻种植模式的高精度制图框架。通过整合Sentinel-1雷达后向散射（VV\u002FVH极化）、Sentinel-2光学指数（包括归一化差异植被指数NDVI和地表水体指数LSWI）以及地形因子（DEM和坡度），构建了多时相、多源特征集，用于分类三种主要种植制度，即麦–稻轮作、双季稻和油–稻轮作。利用2024—2025年生长季采集的548个实地调查样点进行模型训练与验证。系统比较了三种经典分类器——随机森林（RF）、梯度提升树（GBTREE）和支持向量机（SVM）。消融实验表明，Sentinel-1与Sentinel-2的融合在三种分类器中均优于仅使用Sentinel-1或仅使用Sentinel-2的配置。其中，GBTREE在本实验中取得了最高的总体精度（93.8%）、Kappa系数（0.87）和宏平均F1分数（88.3%）。值得注意的是，其在更具挑战性的双季稻类别上同样表现最佳。基于SHapley加法解释（SHAP）的特征重要性分析表明，多时相NDVI物候特征是分类精度的主要驱动因素，雷达后向散射和水体指数提供了必要的补充信息，而地形因子则在区域尺度上起到空间约束作用。基于GBTREE分类得到的空间分布呈现出清晰的格局：麦–稻轮作主导北部平原（苏北、皖北和杭嘉湖平原）；双季稻集中于浙南丘陵和零散的河谷平原；油–稻轮作则呈零散镶嵌状分布。总体而言，本研究表明，在GEE平台上整合多源遥感数据与GBTREE分类器，能够对复杂农业景观中的水稻种植模式进行有效且可扩展的高精度制图。该方法为区域农业结构分析和作物",79,{"impact":18,"substance":152,"depth":17,"authority":111,"freshness":153,"relevant":21,"comment":236},"基于GEE与Sentinel-1\u002F2时序影像结合机器学习实现长三角水稻种植模式高精度制图，方法扎实、结论可靠，对农业遥感监测有参考价值。",[238],{"name":149,"url":231},[26,240,241,28,242],"水稻","机器学习","作物分类",[244,245],"长三角 水稻 种植模式 遥感","Sentinel-1 Sentinel-2 水稻制图","长三角水稻种植模式遥感-3494","10.3390\u002Frs18193302",{"doi":247,"openalex_id":249,"authors":250,"venue":149,"cited_by_count":35,"oa_url":231,"card":271,"direction":54,"ingested_from":55},"W7214147197",[251,254,257,260,262,265,268],{"name":252,"orcid":253},"Xuan Li","https:\u002F\u002Forcid.org\u002F0000-0001-5509-2385",{"name":255,"orcid":256},"Lintao Chen","https:\u002F\u002Forcid.org\u002F0009-0000-6558-1289",{"name":258,"orcid":259},"Lin Chen","https:\u002F\u002Forcid.org\u002F0000-0002-9270-1626",{"name":261,"orcid":8},"Chao Su",{"name":263,"orcid":264},"Hoi Leong Lee","https:\u002F\u002Forcid.org\u002F0000-0002-4984-2183",{"name":266,"orcid":267},"Ruci Wang","https:\u002F\u002Forcid.org\u002F0000-0001-7049-7006",{"name":269,"orcid":270},"Xuguang Tang","https:\u002F\u002Forcid.org\u002F0009-0008-3494-8867",{"tldr":272,"method":273,"finding":274,"direction":54,"opportunity":275},"基于GEE融合Sentinel-1\u002F2时序与地形特征，用机器学习高精度制图长三角水稻种植模式。","GEE平台、Sentinel-1\u002F2时序特征、DEM、548个实地样本、RF\u002FG","GBTREE精度最高（总体93.8%、Kappa 0.87），双季稻识别最好；NDVI物候特征贡献最","可探索样本稀缺区迁移学习与多作物轮作模式泛化制图，并耦合产量与碳核算。","2026-09-25T23:30:30.511522Z",{"id":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":8,"source_name":283,"source_url":280,"published_at":284,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":285,"score_detail":286,"sources":289,"tags":291,"search_phrases":295,"slug":298,"view_count":35,"doi":299,"paper":300,"created_at":315},3488,"NDAVI Improves Genotypic Discrimination in Dense Bread Wheat Canopies under Moderate Nitrogen Contrast","https:\u002F\u002Fdoi.org\u002F10.29278\u002Fazd.1972563","Objective: Rapid and non-destructive assessment of wheat canopy status is important for improving nitrogen management and field-based phenotyping. This study evaluated the ability of UAV-derived multispectral vegetation indices to detect canopy spectral variation associated with a moderate, non-zero nitrogen contrast and to discriminate among commercial bread wheat genotypes under dense canopy conditions during the reproductive stage. Materials and Methods: A field experiment was conducted during the 2024–2025 growing season in Bornova, İzmir, Türkiye, using ten commercial bread wheat cultivars grown under low-nitrogen (LN) and high-nitrogen (HN) treatments in a split-plot randomized complete block design with four replications. Multispectral imagery was acquired after heading and before anthesis using a DJI Matrice 350 RTK UAV equipped with a MicaSense RedEdge-P sensor. Four canopy vegetation indices, NDVI, GNDVI, CLRED, and NDAVI, were calculated from plot-level reflectance data. Each index was analyzed using linear mixed models, and index consistency was further evaluated using coefficient of variation, repeatability, and genotype-specific plasticity.Conclusion: Nitrogen treatment significantly affected NDVI, GNDVI, and NDAVI, with higher index values under HN than LN, whereas CLRED was not significantly affected by nitrogen. Among the evaluated indices, NDVI showed the strongest nitrogen response, indicating its usefulness for detecting overall nitrogen-related canopy differences. However, genotype effects were significant only for NDAVI, while NDVI, GNDVI, and CLRED did not provide reliable genotypic discrimination. NDAVI also showed the highest repeatability (0.791) and the lowest coefficient of variation (1.14%), supporting its superior consistency and discriminatory ability. UAV-derived multispectral indices successfully detected canopy spectral responses to moderate nitrogen differences in bread wheat, but nitrogen sensitivity and genotype discrimination differed substantially among indices. Among the evaluated indices, NDAVI appears to be the most promising index for detecting differences among wheat genotypes, particularly when conventional indices such as NDVI may be constrained by saturation.","目的：快速、无损地评估小麦冠层状态对于改进氮素管理和田间表型分析具有重要意义。本研究评估了无人机多光谱植被指数在生殖阶段密集冠层条件下检测与中等非零氮素差异相关的冠层光谱变异以及区分商业面包小麦基因型的能力。材料与方法：田间试验于2024—2025生长季在土耳其伊兹密尔博尔诺瓦进行，采用10个商业面包小麦品种，设置低氮（LN）和高氮（HN）处理，采用裂区随机完全区组设计，4次重复。在抽穗后至开花前，使用搭载MicaSense RedEdge-P传感器的DJI Matrice 350 RTK无人机获取多光谱影像。基于小区尺度反射率数据计算了4个冠层植被指数：NDVI、GNDVI、CLRED和NDAVI。各指数采用线性混合模型进行分析，并通过变异系数、重复性和基因型特异性可塑性进一步评估指数一致性。结论：氮素处理显著影响NDVI、GNDVI和NDAVI，高氮处理下指数值高于低氮处理，而CLRED未受氮素的显著影响。在评估的指数中，NDVI表现出最强的氮素响应，表明其可用于检测整体氮素相关的冠层差异。然而，基因型效应仅对NDAVI显著，而NDVI、GNDVI和CLRED未能提供可靠的基因型区分。NDAVI还表现出最高的重复性（0.791）和最低的变异系数（1.14%），支持其更优的一致性和区分能力。无人机多光谱指数成功检测了面包小麦对中等氮素差异的冠层光谱响应，但不同指数的氮素敏感性和基因型区分能力存在显著差异。在评估的指数中，NDAVI似乎是检测小麦基因型差异最有前景的指数，尤其是当NDVI等常规指数可能受到饱和限制时。","Akademik Ziraat Dergisi","2026-09-22T00:00:00Z",72,{"impact":16,"substance":287,"depth":71,"authority":19,"freshness":153,"relevant":21,"comment":288},21,"基于无人机多光谱的田间试验，提出NDAVI在密植小麦冠层中优于NDVI的基因型判别能力，方法新颖、数据扎实，对智慧农业表型与氮肥精准管理有参考价值。",[290],{"name":283,"url":280},[26,292,28,293,294],"小麦","氮肥管理","无人机表型",[296,297],"NDAVI 小麦 基因型判别","无人机 多光谱 小麦 氮素","NDAVI小麦基因型判别-3488","10.29278\u002Fazd.1972563",{"doi":299,"openalex_id":301,"authors":302,"venue":283,"cited_by_count":35,"oa_url":309,"card":310,"direction":54,"ingested_from":55},"W7214030909",[303,306],{"name":304,"orcid":305},"Deniz İştipliler","https:\u002F\u002Forcid.org\u002F0000-0002-0887-1121",{"name":307,"orcid":308},"Aliye YILDIRIM","https:\u002F\u002Forcid.org\u002F0000-0002-8101-0803","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6117047",{"tldr":311,"method":312,"finding":313,"direction":54,"opportunity":314},"评估无人机多光谱植被指数在密植小麦中区分氮处理和基因型的能力。","无人机多光谱影像，四种指数，线性混合模型与重复性分析。","NDAVI重复性最高且能区分基因型，NDVI对氮响应最强但无法区分基因型。","可探索NDAVI在其它作物或更高密度冠层中的基因型区分能力及抗饱和机制。","2026-09-25T23:30:24.042515Z"]