[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3607":3,"related-3607":58},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":57},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。",null,"Biosystems Engineering","2026-09-26T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,8,1,"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","苹果","遥感","果园管理","精准灌溉",[33,34],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607",0,"10.1016\u002Fj.biosystemseng.2026.104603",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":50,"direction":54,"ingested_from":56},"W7214458453",[41,44,47],{"name":42,"orcid":43},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":45,"orcid":46},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":48,"orcid":49},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":51,"method":52,"finding":53,"direction":54,"opportunity":55},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","农业遥感与作物表型","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","openalex","2026-09-27T23:30:09.907348Z",{"total":59,"page":22,"page_size":59,"items":60},6,[61,99,145,185,234,272],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":73,"tags":75,"search_phrases":77,"slug":80,"view_count":36,"doi":81,"paper":82,"created_at":98},2155,"Bridging canopy light interception and absorption: Toward a multi-scale radiometric framework for precision irrigation in woody crops","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.srs.2026.100505",": Optimizing irrigation in orchards is increasingly challenged by climatic variability, water scarcity, and structural heterogeneity, requiring indicators that robustly link canopy architecture with transpiration and energy exchange processes. The fraction of intercepted photosynthetically active radiation (fIPAR) is widely used in agronomy to characterize canopy structure and radiation interception, whereas the fraction of absorbed PAR (fAPAR) is more commonly used in remote sensing, yet their relationship and relevance for orchard irrigation remain insufficiently clarified. This review critically examines the theoretical foundations, methodological developments, and irrigation applications of Canopy Light Interception in woody crops over the past three decades. We synthesize empirical formulations, geometric approaches, radiative transfer modelling, and advances in proximal and remote sensing, including hemispherical photography, LiDAR platforms, unmanned aerial vehicles, and satellite-derived products. The conditions under which fIPAR approximates fAPAR are examined, revealing that their divergence is more relevant in discontinuous orchard systems than in homogeneous canopies. We evaluate how Canopy Light Interception supports evapotranspiration estimation, crop coefficient refinement, and irrigation scheduling, while emphasizing that it constrains potential radiative demand rather than directly representing physiological regulation of transpiration. Despite substantial methodological progress, inconsistent terminology, heterogeneous protocols, and limited cross-validation across scales hinder comparability and deployment. We suggest a multi-scale framework in which fIPAR is a structural–radiative constraint on potential canopy water demand. This is embedded within integrated sensing and irrigation decision systems. We argue that clarifying the conceptual boundary between fIPAR and fAPAR is essential for translating canopy radiation assessments into decision-support tools for water-efficient orchard management under increasing climatic pressure.","果园灌溉优化正日益受到气候变异性、水资源短缺和结构异质性的挑战，亟需能够稳健地将冠层结构与蒸腾及能量交换过程联系起来的指标。截获光合有效辐射比例（fIPAR）在农学中被广泛用于表征冠层结构和辐射截获，而吸收光合有效辐射比例（fAPAR）则更常用于遥感领域，但二者之间的关系及其对果园灌溉的相关性仍未被充分阐明。本文综述了过去三十年间木本作物冠层光截获的理论基础、方法学发展及灌溉应用。我们综合了经验公式、几何方法、辐射传输模型以及近地和遥感技术的进展，包括半球摄影、LiDAR平台、无人机和卫星衍生产品。本文考察了fIPAR近似fAPAR的条件，揭示出二者的差异在不连续的果园系统中比在均质冠层中更为显著。我们评估了冠层光截获如何支持蒸散估算、作物系数优化和灌溉调度，同时强调其约束的是潜在辐射需求，而非直接表征蒸腾的生理调控。尽管方法学取得了实质性进展，但术语不一致、方案异质性和跨尺度交叉验证有限等问题阻碍了可比性和实际部署。我们提出一个多尺度框架，其中fIPAR作为冠层潜在水分需求的结构-辐射约束，并将其嵌入集成传感与灌溉决策系统。我们认为，厘清fIPAR与fAPAR之间的概念边界，对于将冠层辐射评估转化为在日益增大的气候压力下实现节水型果园管理的决策支持工具至关重要。","Science of Remote Sensing","2026-09-08T00:00:00Z",79,{"impact":19,"substance":71,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":72},21,"该综述系统梳理了木本作物冠层光截获与吸收的遥感方法，提出多尺度辐射框架以支撑精准灌溉决策，方法学新颖且对果园水分管理有直接指导价值，值得进入每日精选。",[74],{"name":67,"url":64},[27,29,30,31,76],"冠层光截获",[78,79],"冠层光截获 智慧农业 果园管理 精准灌溉","冠层光截获 智慧农业","冠层光截获智慧农业果园管理精准灌溉-2155","10.1016\u002Fj.srs.2026.100505",{"doi":81,"openalex_id":83,"authors":84,"venue":67,"cited_by_count":36,"oa_url":91,"card":92,"direction":97,"ingested_from":56},"W7211970568",[85,88],{"name":86,"orcid":87},"Mohamed Ibrahim Belaid","https:\u002F\u002Forcid.org\u002F0009-0002-9327-4598",{"name":89,"orcid":90},"Jaume Casadesús","https:\u002F\u002Forcid.org\u002F0000-0001-7036-8212","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666017226001434\u002Fpdf",{"tldr":93,"method":94,"finding":95,"direction":54,"opportunity":96},"综述木质作物冠层光截获与吸收关系，提出多尺度辐射框架支撑精准灌溉。","综述三十年经验公式、辐射传输模型及半球摄影、LiDAR、无人机、卫星遥感。","fIPAR与fAPAR在离散果园差异显著，fIPAR应作为潜在需水约束而非蒸腾调控。","可研究果园fIPAR-fAPAR跨尺度转换模型，并耦合遥感与灌溉决策系统验证。","智慧农业 \u002F 农业物联网","2026-09-11T23:30:17.628726Z",{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":110,"tags":112,"search_phrases":115,"slug":118,"view_count":36,"doi":119,"paper":120,"created_at":144},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing","2026-09-15T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":109},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[111],{"name":105,"url":102},[27,113,114,29,31],"农业人工智能","小麦",[116,117],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":119,"openalex_id":121,"authors":122,"venue":105,"cited_by_count":36,"oa_url":102,"card":139,"direction":54,"ingested_from":56},"W7213246708",[123,126,128,130,133,136],{"name":124,"orcid":125},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":127,"orcid":9},"Qian Cheng",{"name":129,"orcid":9},"Fuyi Duan",{"name":131,"orcid":132},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":134,"orcid":135},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":137,"orcid":138},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":140,"method":141,"finding":142,"direction":54,"opportunity":143},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":146,"title":147,"url":148,"summary":149,"summary_zh":150,"content":9,"source_name":151,"source_url":148,"published_at":152,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":153,"score_detail":154,"sources":159,"tags":161,"search_phrases":164,"slug":167,"view_count":36,"doi":168,"paper":169,"created_at":184},2437,"Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52151\u002Fjae2026634.2043","Soil moisture is an important variable influencing agricultural productivity, hydrological processes, and land management, particularly in high-rainfall regions such as the North Eastern Hill (NEH) States of India. Although conventional soil moisture measurement techniques provide reliable observations, they are time-consuming, labour-intensive and limited in spatial coverage, restricting their applicability for regional-scale monitoring. Remote sensing integrated with machine learning provides a promising alternative for generating spatially continuous and timely soil moisture estimates. This study aimed to predict surface soil moisture in the Ranipool-Rumtek administrative region of Sikkim, India, using multimodal remote sensing data and machine-learning techniques. Soil moisture was measured at 85 locations using the gravimetric method and these same locations were used as ground truthing sites. Multi-temporal imagery from Landsat-8 and Sentinel-2 was processed to derive vegetation and moisture-related indices, i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Shortwave-infrared Difference Soil Moisture Index (NSDSI3), Land Surface Temperature (LST), Moisture Stress Index (MSI), and Vegetation Supply Water Index (VSWI). These indices were used as predictor variables to develop artificial neural network (ANN), support vector machine (SVM), and multiple linear regression (MLR) models. The results revealed that the ANN model developed using Sentinel-2-derived indices exhibited the highest predictive accuracy, achieving values of coefficient of determination (R2) as 0.82, Root Mean Square Error (RMSE) as 6% and Mean Absolute Error (MAE) of 4.6%. The performance of the Sentinel-2-derived ANN model was found to be better than that of the Landsat-8-based ANN model as well as the SVM and MLR models developed using Sentinel-2 data. The strong predictive performance demonstrated the effectiveness of integrating high-resolution Sentinel-2 imagery data with ANN for accurate, scalable, and efficient soil moisture estimation in high-relief, data-sparse environments. The proposed approach provides a robust framework to support precision agriculture, irrigation scheduling, hydrological modelling, and drought and flood monitoring.","土壤水分是影响农业生产力、水文过程和土地管理的重要变量，尤其是在印度东北丘陵邦（NEH）等高降雨地区。尽管传统土壤水分测量技术能够提供可靠的观测数据，但其耗时、费力且空间覆盖有限，限制了其在区域尺度监测中的适用性。遥感与机器学习相结合，为生成空间连续且及时的土壤水分估算提供了一种有前景的替代方案。本研究旨在利用多模态遥感数据和机器学习技术，预测印度锡金邦拉尼普尔-鲁姆特克行政区的表层土壤水分。采用重量法在85个位置测量了土壤水分，并将这些位置作为地面验证点。对Landsat-8和Sentinel-2的多时相影像进行处理，以提取植被和水分相关指数，即归一化差异植被指数（NDVI）、归一化差异水体指数（NDWI）、归一化差异水分指数（NDMI）、归一化短波红外差异土壤水分指数（NSDSI3）、地表温度（LST）、水分胁迫指数（MSI）和植被供水指数（VSWI）。这些指数被用作预测变量，以构建人工神经网络（ANN）、支持向量机（SVM）和多元线性回归（MLR）模型。结果表明，使用Sentinel-2衍生指数构建的ANN模型预测精度最高，决定系数（R²）达到0.82，均方根误差（RMSE）为6%，平均绝对误差（MAE）为4.6%。Sentinel-2衍生的ANN模型性能优于基于Landsat-8的ANN模型以及使用Sentinel-2数据构建的SVM和MLR模型。较强的预测性能表明，将高分辨率Sentinel-2影像数据与ANN相结合，能够在地形起伏大、数据稀疏的环境中实现准确、可扩展且高效的土壤水分估算。所提出的方法为支持精准农业、灌溉调度、水文建模以及旱涝监测提供了一个稳健的框架。","Journal of Agricultural Engineering (India)","2026-09-11T00:00:00Z",70,{"impact":17,"substance":155,"depth":156,"authority":157,"freshness":59,"relevant":22,"comment":158},20,17,12,"基于多模态遥感与人工神经网络的土壤墒情预测研究，方法新颖、精度可靠，对高降雨山区精准农业与灌溉调度有参考价值，但属区域性案例，影响范围有限。",[160],{"name":151,"url":148},[27,162,29,31,163],"机器学习","土壤墒情",[165,166],"土壤墒情 智慧农业 机器学习 精准灌溉","土壤墒情 智慧农业","土壤墒情智慧农业机器学习精准灌溉-2437","10.52151\u002Fjae2026634.2043",{"doi":168,"openalex_id":170,"authors":171,"venue":151,"cited_by_count":36,"oa_url":9,"card":179,"direction":54,"ingested_from":56},"W7212454193",[172,174,177],{"name":173,"orcid":9},"Pranjal Dubey",{"name":175,"orcid":176},"G. T. Patle","https:\u002F\u002Forcid.org\u002F0000-0002-9175-8567",{"name":178,"orcid":9},"Vinay Kumar Gautam",{"tldr":180,"method":181,"finding":182,"direction":54,"opportunity":183},"用多模态遥感与机器学习预测印度锡金高降雨区表层土壤水分。","Landsat-8与Sentinel-2植被\u002F水分指数，ANN、SVM、MLR建","Sentinel-2指数驱动的ANN精度最高，R²=0.82、RMSE=6%，优于Landsat-8","可探索多源时序遥感与深度学习融合，提升高降雨山区土壤水分时空连续估算与业务化监测能力。","2026-09-14T23:30:27.771449Z",{"id":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":197,"tags":199,"search_phrases":202,"slug":205,"view_count":36,"doi":206,"paper":207,"created_at":233},2318,"Estimation of field-scale crop evapotranspiration from the mechanistic SIF-ET model using the UAV hyperspectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128332","Accurate estimation of evapotranspiration (ET) is critical for agricultural water management and understanding land-atmosphere interactions. Conventional thermal and optical remote sensing is a powerful tool for ET estimation, but existing methods remain constrained by empirical parameterization, calibration complexity, and limited applicability. As a direct indicator of vegetation photosynthesis, solar-induced chlorophyll fluorescence (SIF) enables consistent and accurate ET estimation through a mechanistically grounded framework that does not require site-specific empirical calibration of the SIF-GPP relationship. However, current SIF-based ET estimation mainly rely on coarse-resolution satellite data and its capacity to resolve high-resolution ET dynamics over farmland remains unexplored. To address this, the mechanistic light response model and water-carbon relationship was integrated to construct a SIF-ET model at field-scale using UAV narrow-band hyperspectra imagery. The principal conclusions are: (1) High-resolution crop (canola,soybean and wheat) canopy SIF 740 at 1-nm SR was retrieved from UAV hyperspectra imagery using the Photochemistry and Energy Fluxes (SCOPE) model; (2)The daily ET estimates from SIF-ET model were validated against the in-situ measurements across multiple growth stages and water-nitrogen treatments, with wheat exhibiting the highest precision (R²=0.72–0.94, RMSE = 0.04–0.11 mm\u002Fd, MAPE = 1.70–5.89%), followed by canola and soybean; (3) Cumulative ET errors across repeated UAV observations within each growth stage remained within acceptable bounds, indicating the stability of SIF-ET model in capturing long-term ET dynamics. Overall, the SIF-ET model from UAV hyperspectra imagery revealed fine-scale cropland ET spatial-temporal heterogeneity. These findings provide support for precision irrigation, rational water-nitrogen management, and stress diagnostics under changing environmental conditions.","准确估算蒸散发（ET）对农业水资源管理和理解陆气相互作用至关重要。传统的热红外与光学遥感是估算ET的有力工具，但现有方法仍受限于经验参数化、校准复杂性和适用性有限等问题。作为植被光合作用的直接指示因子，日光诱导叶绿素荧光（SIF）能够通过机理明确的框架实现一致且准确的ET估算，且无需对SIF-GPP关系进行站点特定的经验校准。然而，当前基于SIF的ET估算主要依赖粗分辨率卫星数据，其解析农田高分辨率ET动态的能力尚未得到探索。为此，本研究整合机理光响应模型与水碳关系，利用无人机窄波段高光谱影像构建了田块尺度的SIF-ET模型。主要结论如下：（1）利用光化学与能量通量（SCOPE）模型，从无人机高光谱影像中反演了1 nm光谱分辨率下油菜、大豆和小麦的高分辨率冠层SIF₇₄₀；（2）基于SIF-ET模型的日ET估算值在多个生育期和水氮处理下与原位观测进行了验证，其中小麦精度最高（R²=0.72–0.94，RMSE=0.04–0.11 mm\u002Fd，MAPE=1.70–5.89%），油菜和大豆次之；（3）各生育期内重复无人机观测的累积ET误差均在可接受范围内，表明SIF-ET模型在捕捉长期ET动态方面具有稳定性。总体而言，基于无人机高光谱影像的SIF-ET模型揭示了精细尺度的农田ET时空异质性。这些发现为精准灌溉、合理水氮管理以及变化环境条件下的胁迫诊断提供了支持。","European Journal of Agronomy","2026-09-12T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":195,"relevant":22,"comment":196},9,"该研究基于无人机高光谱与SIF-ET机理模型实现田块尺度作物蒸散发高精度估算，方法新颖、验证充分，对精准灌溉与水氮管理有实质参考价值，值得进入每日精选。",[198],{"name":191,"url":188},[27,200,29,31,201],"无人机","蒸散发",[203,204],"智慧农业 精准灌溉 无人机 蒸散发","智慧农业 精准灌溉","智慧农业精准灌溉无人机蒸散发-2318","10.1016\u002Fj.eja.2026.128332",{"doi":206,"openalex_id":208,"authors":209,"venue":191,"cited_by_count":36,"oa_url":188,"card":228,"direction":54,"ingested_from":56},"W7212242584",[210,212,214,217,220,222,225],{"name":211,"orcid":9},"Ruiqi Du",{"name":213,"orcid":9},"Yonghong Zhang",{"name":215,"orcid":216},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":218,"orcid":219},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":221,"orcid":9},"Jian Gao",{"name":223,"orcid":224},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":226,"orcid":227},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",{"tldr":229,"method":230,"finding":231,"direction":54,"opportunity":232},"利用无人机高光谱影像构建田间尺度SIF-ET机理模型，实现作物蒸散发高精度估算。","无人机窄波段高光谱结合SCOPE模型反演SIF，耦合光响应与水碳关系构建SIF-","小麦估算精度最高（R²=0.72–0.94），模型能稳定捕捉多生育期蒸散发动态与空间异质性。","可探索多作物多环境下的SIF-ET普适性，并融合热红外与机器学习提升胁迫诊断能力。","2026-09-13T23:30:22.677041Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":240,"source_url":237,"published_at":241,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":242,"score_detail":243,"sources":246,"tags":248,"search_phrases":250,"slug":253,"view_count":36,"doi":254,"paper":255,"created_at":271},1905,"A novel surface reflectance calibration approach to improve the estimation of maize actual evapotranspiration in a semi-arid USA region","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727171","This study aimed to develop a novel surface reflectance (SR) data calibration approach to improve maize actual crop evapotranspiration (ETa, mm h−1) predictions in agricultural settings. The remote sensing (RS) platforms used in the study included Landsat-8 (30 m spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), a handheld radiometer (1 m), and a small uncrewed aerial system or sUAS (0.03 m). A one-source surface energy balance (OSEB) was applied to estimate ETa and assess any improvements when using original and calibrated SR data. Five machine-learning (ML) algorithms were evaluated as part of the calibration process. The ML approaches were linear regression, regression tree, random forest, support vector machine, and Gaussian process regression. The SR calibration approach incorporated a novel dual-source pixel decomposition model that accounts for the contributions of soil and vegetation light reflectance captured by a given SR band. Two maize research sites, one in Greeley and another in Fort Collins, Colorado, U.S.A. provided the grounds for data collection over a four-year period. Results indicated that the best ML model, for a given RS sensor, depended on the SR contributions from plants and bare soil. Among the evaluated algorithms, the Regression Tree and Gaussian Process Regression provided the most accurate pixel decomposition and reflectance adjustments, consistently demonstrating the highest statistical agreement (R2 = 0.70–0.94) and lowest residual errors (RMSE = 0.004–0.022). Improvements in maize hourly ETa had a 34% error reduction in maize ETa estimation for data from the handheld radiometer and sUAS, compared to a 12% error reduction for the spaceborne sensors, on average.","本研究旨在开发一种新型地表反射率（SR）数据校准方法，以提高农业环境中玉米实际作物蒸散量（ETa, mm h⁻¹）的预测精度。研究中使用的遥感（RS）平台包括Landsat-8（空间分辨率30米）、Sentinel-2（10米）、Planet CubeSat（3米）、手持式辐射计（1米）以及小型无人机系统（sUAS，0.03米）。采用单源地表能量平衡（OSEB）模型估算ETa，并评估使用原始与校准SR数据时的改进效果。校准过程中评估了五种机器学习（ML）算法，包括线性回归、回归树、随机森林、支持向量机和高斯过程回归。SR校准方法引入了一种新型双源像元分解模型，该模型考虑了特定SR波段所捕获的土壤与植被光反射的贡献。研究选取了美国科罗拉多州格里利和柯林斯堡的两个玉米研究站点，在四年期间收集数据。结果表明，对于给定的遥感传感器，最佳ML模型取决于植被和裸土的SR贡献。在所评估的算法中，回归树和高斯过程回归提供了最精确的像元分解和反射率调整，始终表现出最高的一致性统计指标（R² = 0.70–0.94）和最低的残差误差（RMSE = 0.004–0.022）。在玉米小时ETa估算改进方面，手持式辐射计和sUAS数据的ETa估算误差平均降低了34%，而星载传感器的误差平均降低了12%。","International Journal of Remote Sensing","2026-09-06T00:00:00Z",72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":244,"relevant":22,"comment":245},3,"研究提出地表反射率校准新方法，结合多源遥感与机器学习提升玉米蒸散估算精度，对农业水资源管理有参考价值。",[247],{"name":240,"url":237},[27,162,29,249,31],"作物模型",[251,252],"作物模型 智慧农业 机器学习 精准灌溉","作物模型 智慧农业","作物模型智慧农业机器学习精准灌溉-1905","10.1080\u002F01431161.2026.2727171",{"doi":254,"openalex_id":256,"authors":257,"venue":240,"cited_by_count":36,"oa_url":9,"card":266,"direction":54,"ingested_from":56},"W7210284621",[258,261,264],{"name":259,"orcid":260},"Edson Costa‐Filho","https:\u002F\u002Forcid.org\u002F0000-0001-5610-7382",{"name":262,"orcid":263},"José L. Chávez","https:\u002F\u002Forcid.org\u002F0000-0001-6456-0822",{"name":265,"orcid":9},"Huihui Zhang",{"tldr":267,"method":268,"finding":269,"direction":54,"opportunity":270},"提出地表反射率校准方法，结合机器学习与双源像素分解，提高半干旱区玉米蒸散发估算精度。","多源遥感数据，OSEB模型，五种机器学习算法，双源像素分解模型。","回归树和高斯过程回归校准效果最佳，误差降低12%-34%，高分辨率传感器提升更显著。","可探索将校准方法应用于其他作物或区域，或结合深度学习提升低分辨率卫星数据的校准精度。","2026-09-08T23:30:20.154043Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":9,"source_name":278,"source_url":275,"published_at":279,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":280,"score_detail":281,"sources":283,"tags":285,"search_phrases":287,"slug":289,"view_count":22,"doi":290,"paper":291,"created_at":321},1895,"Continuous monitoring of deficit irrigation in avocado across two contrasting rainfall years using sensor networks, telemetry, and machine learning","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.03.745400","Water scarcity and increasingly irregular rainfall threaten avocado production in Mediterranean regions, yet the long term physiological responses of mature trees to sustained deficit irrigation remain poorly understood. We conducted a two-year field study integrating continuous monitoring of the soil plant atmosphere continuum, drone-based multispectral imaging, canopy structural analysis, and fruit phenotyping in a mature avocado orchard subjected to three irrigation regimes. The two study years differed markedly in rainfall, providing a unique opportunity to evaluate how environmental conditions modulate tree responses to water limitation. Trees under severe deficit irrigation showed depletion of water in deeper soil layers and a flattened physiological profile, with near-zero diel variation in leaf thickness and trunk water potential, indicating minimal transpiration and decoupling of tree water status from environmental demand. Drone telemetry via NDVI detected stress during fruit growth and maturation, but not during flowering or the new summer leaf flush, revealing greater drought sensitivity at later maturation stages. Although canopy area did not differ among irrigation treatments, canopy surface roughness increased significantly under deficit irrigation, thereby identifying a novel structural indicator of drought stress. Despite large physiological differences among treatments, fruit number remained stable, while fruit weight decreased significantly under severe deficit irrigation, particularly in the wetter year, suggesting that annual rainfall modulates the trade-off between fruit retention and fruit growth. This study provides the first continuous, multi-scale characterization of avocado performance under sustained deficit irrigation in Mediterranean conditions. By integrating plant-based sensors, remote sensing, and artificial intelligence, we reveal previously undescribed stress dynamics and identify new indicators for precision irrigation management in fruit crops.","水资源短缺和日益不规律的降雨威胁着地中海地区鳄梨的生产，然而，长期亏缺灌溉下成熟树木的生理响应仍知之甚少。我们开展了一项为期两年的田间研究，在成熟鳄梨园中设置了三种灌溉制度，整合了土壤-植物-大气连续体的连续监测、无人机多光谱成像、冠层结构分析以及果实表型鉴定。两个研究年份的降雨量差异显著，为评估环境条件如何调节树木对水分限制的响应提供了独特机会。严重亏缺灌溉下的树木表现出深层土壤水分耗竭和生理特征趋于平缓的现象，叶片厚度和树干水势的昼夜变化接近于零，表明蒸腾作用极低，树木水分状态与环境需求脱钩。通过NDVI进行的无人机遥测在果实生长和成熟期检测到胁迫，但在开花期或夏季新梢生长期未检测到，揭示了后期成熟阶段对干旱的敏感性更高。尽管各灌溉处理间冠层面积无显著差异，但亏缺灌溉下冠层表面粗糙度显著增加，从而识别出一种新的干旱胁迫结构指标。尽管各处理间生理差异显著，果实数量保持稳定，而严重亏缺灌溉下果实重量显著下降，尤其在较湿润的年份更为明显，表明年降雨量调节着果实保留与果实生长之间的权衡。本研究首次提供了地中海条件下持续亏缺灌溉下鳄梨表现的多尺度连续表征。通过整合基于植物的传感器、遥感技术和人工智能，我们揭示了此前未被描述的胁迫动态，并为果树精准灌溉管理识别了新指标。","bioRxiv (Cold Spring Harbor Laboratory)","2026-09-07T00:00:00Z",73,{"impact":19,"substance":18,"depth":19,"authority":157,"freshness":244,"relevant":22,"comment":282},"研究利用传感器网络与机器学习持续监测牛油果缺水胁迫，揭示新结构指标，对精准灌溉有重要参考价值。",[284],{"name":278,"url":275},[27,113,29,31,286],"牛油果",[288,117],"农业人工智能 智慧农业 精准灌溉 牛油果","农业人工智能智慧农业精准灌溉牛油果-1895","10.64898\u002F2026.09.03.745400",{"doi":290,"openalex_id":292,"authors":293,"venue":278,"cited_by_count":36,"oa_url":315,"card":316,"direction":54,"ingested_from":56},"W7211887459",[294,297,300,303,306,309,312],{"name":295,"orcid":296},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":298,"orcid":299},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":301,"orcid":302},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":304,"orcid":305},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":307,"orcid":308},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":310,"orcid":311},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":313,"orcid":314},"Juan M. Losada","https:\u002F\u002Forcid.org\u002F0000-0002-7966-5018","https:\u002F\u002Fwww.biorxiv.org\u002Fcontent\u002Fbiorxiv\u002Fearly\u002F2026\u002F09\u002F07\u002F2026.09.03.745400.full.pdf",{"tldr":317,"method":318,"finding":319,"direction":97,"opportunity":320},"通过传感器、遥感和机器学习连续监测两年不同降雨下鳄梨的亏缺灌溉响应。","集成土壤-植物-大气连续体监测、无人机多光谱、冠层分析及机器学习。","严重亏缺灌溉导致深层土壤水分耗尽、生理活动减弱，果实重量下降，冠层表面粗糙度增加。","可研究不同降雨年份下亏缺灌溉对果实品质的影响，或开发基于冠层粗糙度的干旱胁迫预警模型。","2026-09-08T23:30:14.886372Z"]