[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2432":3,"related-2432":85},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":30,"classification":31,"search_phrases":58,"slug":61,"view_count":21,"doi":62,"paper":63,"created_at":84},2432,"Multi-annual sub-pixel land cover mapping with TESSERA latent embeddings","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx59j7c","Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. Sub-pixel land cover fraction maps based on medium-resolution optical remote sensing data like Landsat or Sentinel-2 overcome this limitation but are often not consistently available across multiple years because creating temporally robust and comparable multi-class fraction models can be challenging. Latent embeddings from geospatial foundation models are promising as they represent high-dimensional, large-scale general-purpose spectral, temporal, and structural features derived from Earth Observation data that are largely independent of available high-quality observations at specific points in time. We here assessed the usability of TESSERA latent embeddings for machine-learning regression-based land cover fraction models and compared the performance to more conventional spectral-temporal metrics and spline coefficients as input features. We also assessed the suitability of these inputs to train temporally transferred and generalized fraction models. We found that TESSERA latent embeddings are a well-suitable input to land cover fraction mapping, performing slightly better than spectral-temporal-metrics-based models in many cases, both being notably outperformed by spline coefficients (MAE across classes and years 11.58 vs. 11.64 vs. 9.45). Spline coefficients also performed best in all temporally generalized and many temporally transferred models. However, models trained with TESSERA latent embeddings demonstrate the highest consistency of transferred models, highlighting their capability to handle data gaps. We suggest that TESSERA latent embeddings are a valuable input to fraction mapping where data availability across years is highly variable, but spline coefficients generally perform best when sufficient high-quality observations are available.","在高度异质的景观中绘制土地覆盖图具有挑战性，而且当遥感数据的空间分辨率超过小对象尺寸时，分类存在固有局限。基于中分辨率光学遥感数据（如Landsat或Sentinel-2）的亚像元土地覆盖比例图克服了这一局限，但往往无法在多个年份间一致获取，因为构建时间上稳健且可比的多类比例模型可能颇具挑战。地理空间基础模型的潜在嵌入（latent embeddings）前景广阔，因为它们代表了从地球观测数据中提取的高维、大规模通用光谱、时间和结构特征，这些特征在很大程度上独立于特定时间点可获取的高质量观测。我们在此评估了TESSERA潜在嵌入用于基于机器学习回归的土地覆盖比例模型的可用性，并将其性能与更传统的光谱-时间指标（spectral-temporal metrics）和样条系数（spline coefficients）作为输入特征进行了比较。我们还评估了这些输入用于训练时间迁移和广义化比例模型的适用性。我们发现，TESSERA潜在嵌入是土地覆盖比例制图的合适输入，在许多情况下略优于基于光谱-时间指标的模型，而两者的表现均明显不及样条系数（跨类别和年份的MAE分别为11.58、11.64和9.45）。样条系数在所有时间广义化模型和许多时间迁移模型中也表现最佳。然而，使用TESSERA潜在嵌入训练的模型在迁移模型间表现出最高的一致性，凸显了其处理数据缺口的能​​力。我们建议，在跨年份数据可用性高度可变的情况下，TESSERA潜在嵌入是比例制图的宝贵输入，但当有足够的高质量观测可用时，样条系数通常表现最佳。",null,"OpenAlex","2026-09-11T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,21,17,8,1,"评估TESSERA地理空间基础模型潜嵌入用于多年份亚像元土地覆盖制图，方法新颖、结论可靠，对农业遥感监测有参考价值，但属细分方法研究，影响面有限。",[24],{"name":10,"url":6},[26,27,28,29],"农业人工智能","遥感","基础模型","土地覆盖",[],{"status":32,"locked":14,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":14,"reason":57},[37,45,52],{"code":38,"score":39,"supported":40,"title":41,"abstract":42,"tags":44},"I3",7,true,[],[27,43],"remote sensing",[27],{"code":46,"score":47,"supported":40,"title":48,"abstract":49,"tags":51},"I2",4,[],[50],"机器学习",[26],{"code":53,"score":21,"supported":14,"title":54,"abstract":55,"tags":56},"H6",[],[],[29],"农业相关性需要复核",[59,60],"农业人工智能 土地覆盖 基础模型 遥感","农业人工智能 土地覆盖","农业人工智能土地覆盖基础模型遥感-2432","10.31223\u002Fx59j7c",{"doi":62,"openalex_id":64,"authors":65,"venue":9,"cited_by_count":75,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7212309976",[66,69,71,73],{"name":67,"orcid":68},"Franz Schug","https:\u002F\u002Forcid.org\u002F0000-0003-1534-5610",{"name":70,"orcid":9},"David Klehr",{"name":72,"orcid":9},"Jari Mahler",{"name":74,"orcid":9},"David Frantz",0,"https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F14912\u002Fdownload\u002F25925\u002F",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"评估TESSERA潜嵌入用于多年度亚像元土地覆盖制图，并与光谱时序指标和样条系数比较。","使用TESSERA潜嵌入、光谱时序指标和样条系数作为特征，训练机器学习回归模型预","样条系数精度最高，但TESSERA潜嵌入在时间迁移模型中一致性最好，适合数据缺失场景。","农业遥感与作物表型","可探索将地理空间基础模型潜嵌入用于多年度作物覆盖比例制图，解决数据不均下的时序泛化问题。","openalex","2026-09-14T23:30:27.289645Z",{"total":86,"page":21,"page_size":86,"items":87},6,[88,164,228,282,397,461],{"id":89,"title":90,"url":91,"summary":92,"summary_zh":93,"content":9,"source_name":94,"source_url":91,"published_at":95,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":96,"score_detail":97,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":113,"classification":114,"search_phrases":131,"slug":134,"view_count":135,"doi":136,"paper":137,"created_at":163},2790,"High-resolution land cover mapping from coarse labels via a noisy label learning-guided cross-scale framework","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15481603.2026.2726002","High-resolution remote sensing images (HRSIs) provide essential data support for land cover mapping, where deep learning has shown great promise. However, deep learning-based methods rely on abundant high-quality annotations, while low-resolution coarse labels are difficult to use directly in HRSIs training. In this paper, a novel noisy label learning-guided cross-scale framework (NL-CSF) is proposed, which is designed to achieve high-resolution land cover mapping from coarse labels. First, a spectral-based label mask filtering strategy is developed to preliminarily optimize coarse labels. Then, an adaptive noise evaluation scheme is introduced that assigns loss weights based on the noise differences between image and label patches in the training set. Finally, we design a cross-scale transfer Transformer (CSTT) model based on the vision Transformer (ViT) architecture, and the training process is guided by a noise-weighted loss function. Two cross-scale datasets are utilized to evaluate the performance of NL-CSF in multiple spatial scale differences (10 m to 3 m, 3 m to 0.5 m, and 10 m to 0.5 m). Experimental results demonstrate that NL-CSF improves overall accuracy (OA) by at least 7%, 6%, and 4% across the three cross-scale tasks in the first dataset, and by at least 2%, 9%, and 4% in the second dataset, respectively, compared with existing methods. Furthermore, the proposed framework is applied to cross-scale mapping across Jianye District of Nanjing (urban), Sheyang County of Yancheng (agricultural), and the Yellow River Delta of Dongying (wetland), leveraging a low-resolution land cover product and high-resolution PlanetScope images to generate more precise land cover maps. These results demonstrate the effectiveness of the proposed framework in mitigating the impact of noisy coarse labels and generating reliable high-resolution land cover maps.","高分辨率遥感影像(high-resolution remote sensing images, HRSIs)为土地覆盖制图提供了重要的数据支撑，深度学习在此领域展现出巨大潜力。然而，基于深度学习的方法依赖于大量高质量标注，而低分辨率粗标签难以直接用于高分辨率遥感影像训练。本文提出了一种新的噪声标签学习引导的跨尺度框架(noisy label learning-guided cross-scale framework, NL-CSF)，旨在从粗标签实现高分辨率土地覆盖制图。首先，提出了一种基于光谱的标签掩膜过滤策略，对粗标签进行初步优化。然后，引入了一种自适应噪声评估方案，根据训练集中影像块与标签块之间的噪声差异分配损失权重。最后，基于视觉Transformer(vision Transformer, ViT)架构设计了跨尺度迁移Transformer(cross-scale transfer Transformer, CSTT)模型，并以噪声加权损失函数引导训练过程。利用两个跨尺度数据集评估NL-CSF在多种空间尺度差异(10 m至3 m、3 m至0.5 m、10 m至0.5 m)下的性能。实验结果表明，与现有方法相比，NL-CSF在第一个数据集的三个跨尺度任务中总体精度(overall accuracy, OA)分别至少提升7%、6%和4%，在第二个数据集中分别至少提升2%、9%和4%。此外，将所提框架应用于南京建邺区(城市)、盐城射阳县(农业)和东营黄河三角洲(湿地)的跨尺度制图，利用低分辨率土地覆盖产品和高分PlanetScope影像生成更精确的土地覆盖图。这些结果证明了所提框架在减轻噪声粗标签影响和生成可靠高分辨率土地覆盖图方面的有效性。","GIScience & Remote Sensing","2026-09-16T00:00:00Z",81,{"impact":98,"substance":99,"depth":98,"authority":100,"freshness":101,"relevant":21,"comment":102},18,22,14,9,"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图，精度提升显著，对农业遥感监测有实用价值。",[104],{"name":94,"url":91},[26,106,27,29,107],"深度学习","高分辨率制图",{"code":46,"slug":109,"name":110,"path":111,"parent_code":112},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":115,"locked":14,"version":33,"method":34,"evidence":116},"classified",{"candidates":117,"agricultural_evidence":40},[118,123,127],{"code":46,"score":20,"supported":40,"title":119,"abstract":120,"tags":122},[],[106,121],"deep learning",[26,106],{"code":38,"score":39,"supported":40,"title":124,"abstract":125,"tags":126},[],[27,43],[27],{"code":53,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},[],[],[29],[132,133],"农业人工智能 高分辨率制图 土地覆盖 深度学习","农业人工智能 高分辨率制图","农业人工智能高分辨率制图土地覆盖深度学习-2790",3,"10.1080\u002F15481603.2026.2726002",{"doi":136,"openalex_id":138,"authors":139,"venue":94,"cited_by_count":75,"oa_url":91,"card":158,"direction":81,"ingested_from":83},"W7213283296",[140,143,146,149,152,154,156],{"name":141,"orcid":142},"Xiangyu Nie","https:\u002F\u002Forcid.org\u002F0009-0001-5095-6401",{"name":144,"orcid":145},"Cong Lin","https:\u002F\u002Forcid.org\u002F0000-0001-5386-7343",{"name":147,"orcid":148},"Wei Zhang","https:\u002F\u002Forcid.org\u002F0000-0001-8162-9422",{"name":150,"orcid":151},"Hong Fang","https:\u002F\u002Forcid.org\u002F0000-0003-3707-0910",{"name":153,"orcid":9},"Zhen Dong",{"name":155,"orcid":9},"Sicong Liu",{"name":157,"orcid":9},"Zhaohui Xue",{"tldr":159,"method":160,"finding":161,"direction":81,"opportunity":162},"提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图。","谱掩膜过滤粗标签、自适应噪声评估加权损失、基于ViT的跨尺度迁移Transfor","在多个跨尺度任务上总体精度提升2%-9%，并在城市、农业、湿地场景生成更精确土地覆盖图。","可探索将粗标签跨尺度学习用于作物精细分类与长时序农情监测，降低高精度标注依赖。","2026-09-17T23:30:34.952106Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":175,"tags":177,"primary_topic":181,"secondary_topics":182,"classification":183,"search_phrases":211,"slug":214,"view_count":75,"doi":215,"paper":216,"created_at":227},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":17,"substance":99,"depth":98,"authority":17,"freshness":13,"relevant":21,"comment":174},"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[176],{"name":170,"url":167},[178,26,179,27,180],"智慧农业","粮食安全","气候分析",{"code":46,"slug":109,"name":110,"path":111,"parent_code":112},[],{"status":115,"locked":14,"version":33,"method":34,"evidence":184},{"candidates":185,"agricultural_evidence":40},[186,191,197,202,207],{"code":46,"score":101,"supported":40,"title":187,"abstract":189,"tags":190},[188],"artificial intelligence",[],[26],{"code":192,"score":135,"supported":40,"title":193,"abstract":194,"tags":196},"I5",[],[195],"农业传感",[],{"code":198,"score":21,"supported":14,"title":199,"abstract":200,"tags":201},"A8",[],[],[179],{"code":203,"score":21,"supported":14,"title":204,"abstract":205,"tags":206},"I1",[],[],[178],{"code":38,"score":21,"supported":14,"title":208,"abstract":209,"tags":210},[],[],[27],[212,213],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":215,"openalex_id":217,"authors":218,"venue":170,"cited_by_count":75,"oa_url":167,"card":221,"direction":226,"ingested_from":83},"W7223111766",[219],{"name":220,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":222,"method":223,"finding":224,"direction":81,"opportunity":225},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":170,"source_url":231,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":238,"tags":240,"primary_topic":243,"secondary_topics":244,"classification":249,"search_phrases":266,"slug":269,"view_count":75,"doi":270,"paper":271,"created_at":281},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":20,"substance":236,"depth":19,"authority":17,"freshness":13,"relevant":21,"comment":237},20,"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[239],{"name":170,"url":231},[178,26,241,27,242],"产量预测","可解释性",{"code":46,"slug":109,"name":110,"path":111,"parent_code":112},[245],{"code":38,"slug":246,"name":247,"path":248,"parent_code":112},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":115,"locked":14,"version":33,"method":34,"evidence":250},{"candidates":251,"agricultural_evidence":40},[252,258,262],{"code":46,"score":17,"supported":40,"title":253,"abstract":255,"tags":257},[254],"computer vision",[256],"计算机视觉",[26],{"code":38,"score":20,"supported":40,"title":259,"abstract":260,"tags":261},[],[27,43],[241,27],{"code":203,"score":21,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[178],[267,268],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":270,"openalex_id":272,"authors":273,"venue":170,"cited_by_count":75,"oa_url":231,"card":276,"direction":81,"ingested_from":83},"W7223114002",[274],{"name":275,"orcid":9},"Kadiatou Barry",{"tldr":277,"method":278,"finding":279,"direction":81,"opportunity":280},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":9,"source_name":288,"source_url":285,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":297,"secondary_topics":303,"classification":309,"search_phrases":367,"slug":370,"view_count":75,"doi":371,"paper":372,"created_at":396},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":98,"substance":99,"depth":98,"authority":17,"freshness":101,"relevant":21,"comment":291},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[293],{"name":288,"url":285},[178,26,27,295,296],"作物水分胁迫","精准灌溉",{"code":298,"slug":299,"name":300,"path":301,"parent_code":302},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[304,305],{"code":46,"slug":109,"name":110,"path":111,"parent_code":112},{"code":192,"slug":306,"name":307,"path":308,"parent_code":112},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":115,"locked":14,"version":33,"method":34,"evidence":310},{"candidates":311,"agricultural_evidence":40},[312,318,323,330,336,340,347,354,361],{"code":298,"score":313,"supported":40,"title":314,"abstract":315,"tags":317},15,[299],[316,299],"灌溉",[296],{"code":46,"score":13,"supported":40,"title":319,"abstract":320,"tags":322},[],[50,321,188],"machine learning",[26],{"code":192,"score":101,"supported":40,"title":324,"abstract":325,"tags":329},[],[326,327,328],"物联网","internet of things","IoT",[],{"code":203,"score":39,"supported":40,"title":331,"abstract":332,"tags":335},[],[333,334],"数字孪生","digital twin",[178],{"code":38,"score":39,"supported":40,"title":337,"abstract":338,"tags":339},[],[27,43],[27],{"code":341,"score":86,"supported":40,"title":342,"abstract":343,"tags":346},"A2",[],[344,345],"小麦","wheat",[],{"code":348,"score":86,"supported":40,"title":349,"abstract":350,"tags":353},"A3",[],[351,352],"玉米","maize",[],{"code":355,"score":86,"supported":40,"title":356,"abstract":357,"tags":360},"A5",[],[358,359],"马铃薯","potato",[],{"code":362,"score":135,"supported":40,"title":363,"abstract":364,"tags":366},"B2",[],[365],"葡萄",[],[368,369],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":371,"openalex_id":373,"authors":374,"venue":288,"cited_by_count":75,"oa_url":285,"card":390,"direction":395,"ingested_from":83},"W7223088921",[375,378,381,384,387],{"name":376,"orcid":377},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":379,"orcid":380},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":382,"orcid":383},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":385,"orcid":386},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":388,"orcid":389},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":391,"method":392,"finding":393,"direction":81,"opportunity":394},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":398,"title":399,"url":400,"summary":401,"summary_zh":402,"content":9,"source_name":170,"source_url":400,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":407,"tags":409,"primary_topic":413,"secondary_topics":414,"classification":417,"search_phrases":445,"slug":448,"view_count":75,"doi":449,"paper":450,"created_at":460},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":98,"substance":236,"depth":405,"authority":17,"freshness":101,"relevant":21,"comment":406},16,"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[408],{"name":170,"url":400},[26,410,27,411,412],"农业物联网","智慧灌溉","节水农业",{"code":298,"slug":299,"name":300,"path":301,"parent_code":302},[415,416],{"code":192,"slug":306,"name":307,"path":308,"parent_code":112},{"code":38,"slug":246,"name":247,"path":248,"parent_code":112},{"status":115,"locked":14,"version":33,"method":34,"evidence":418},{"candidates":419,"agricultural_evidence":40},[420,426,430,434,438],{"code":298,"score":421,"supported":40,"title":422,"abstract":423,"tags":425},19,[299],[316,424,299],"水资源",[411,412],{"code":192,"score":98,"supported":40,"title":427,"abstract":428,"tags":429},[328],[326,327,328],[410],{"code":38,"score":313,"supported":40,"title":431,"abstract":432,"tags":433},[43],[27,43],[27],{"code":46,"score":17,"supported":40,"title":435,"abstract":436,"tags":437},[188],[188],[26],{"code":439,"score":86,"supported":40,"title":440,"abstract":441,"tags":444},"H8",[],[442,443],"可持续农业","sustainable agriculture",[],[446,447],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":449,"openalex_id":451,"authors":452,"venue":170,"cited_by_count":75,"oa_url":400,"card":455,"direction":395,"ingested_from":83},"W7223090389",[453],{"name":454,"orcid":9},"Patil",{"tldr":456,"method":457,"finding":458,"direction":395,"opportunity":459},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":9,"source_name":170,"source_url":464,"published_at":467,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":468,"sources":470,"tags":472,"primary_topic":476,"secondary_topics":480,"classification":481,"search_phrases":506,"slug":509,"view_count":75,"doi":510,"paper":511,"created_at":525},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","2026-10-20T00:00:00Z",{"impact":17,"substance":236,"depth":19,"authority":17,"freshness":101,"relevant":21,"comment":469},"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[471],{"name":170,"url":464},[26,27,473,474,475],"GIS","野生动物保护","人兽冲突",{"code":112,"slug":477,"name":478,"path":479,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":482,"locked":14,"version":33,"method":34,"evidence":483},"partial",{"candidates":484,"agricultural_evidence":40,"reason":505},[485,490,497,501],{"code":46,"score":13,"supported":40,"title":486,"abstract":487,"tags":489},[],[50,321,488],"decision support",[26],{"code":491,"score":13,"supported":40,"title":492,"abstract":493,"tags":496},"I4",[],[494,473,495],"地理信息","geographic information",[473],{"code":38,"score":47,"supported":40,"title":498,"abstract":499,"tags":500},[],[27],[27],{"code":53,"score":21,"supported":14,"title":502,"abstract":503,"tags":504},[],[],[474],"一级领域明确，二级方向需要细分",[507,508],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":510,"openalex_id":512,"authors":513,"venue":170,"cited_by_count":75,"oa_url":464,"card":520,"direction":226,"ingested_from":83},"W7222999966",[514,516,518],{"name":515,"orcid":9},"Bhutia Dechen Doma",{"name":517,"orcid":9},"Swarup Das",{"name":519,"orcid":9},"Mandal Rakesh Kumar",{"tldr":521,"method":522,"finding":523,"direction":81,"opportunity":524},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","2026-10-21T23:30:43.936453Z"]