[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2318":3,"related-2318":128},{"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":31,"secondary_topics":37,"classification":50,"search_phrases":92,"slug":95,"view_count":96,"doi":97,"paper":98,"created_at":127},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。",null,"European Journal of Agronomy","2026-09-12T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","无人机","遥感","精准灌溉","蒸散发",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[38,44],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":51,"locked":14,"version":52,"method":53,"evidence":54},"classified","2026-10-22.2","rules",{"candidates":55,"agricultural_evidence":58},[56,65,72,79,87],{"code":32,"score":57,"supported":58,"title":59,"abstract":61,"tags":64},19,true,[60],"evapotranspiration",[62,63,30,33,60],"灌溉","水资源",[29,30],{"code":45,"score":66,"supported":58,"title":67,"abstract":69,"tags":71},15,[68],"hyperspectral",[28,70],"remote sensing",[28],{"code":39,"score":20,"supported":58,"title":73,"abstract":74,"tags":78},[],[75,76,77],"大豆","油菜","soybean",[],{"code":80,"score":81,"supported":58,"title":82,"abstract":83,"tags":86},"A2",6,[],[84,85],"小麦","wheat",[],{"code":88,"score":21,"supported":14,"title":89,"abstract":90,"tags":91},"I1",[],[],[26],[93,94],"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",3,"10.1016\u002Fj.eja.2026.128332",{"doi":97,"openalex_id":99,"authors":100,"venue":10,"cited_by_count":119,"oa_url":6,"card":120,"direction":124,"ingested_from":126},"W7212242584",[101,103,105,108,111,113,116],{"name":102,"orcid":9},"Ruiqi Du",{"name":104,"orcid":9},"Yonghong Zhang",{"name":106,"orcid":107},"Youzhen Xiang","https:\u002F\u002Forcid.org\u002F0000-0002-8268-1609",{"name":109,"orcid":110},"Fucang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-6659-3262",{"name":112,"orcid":9},"Jian Gao",{"name":114,"orcid":115},"Qiliang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-3274-2119",{"name":117,"orcid":118},"Xianghui Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0638-3068",0,{"tldr":121,"method":122,"finding":123,"direction":124,"opportunity":125},"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。","农业遥感与作物表型","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。","openalex","2026-09-13T23:30:22.677041Z",{"total":81,"page":21,"page_size":81,"items":129},[130,220,319,375,451,514],{"id":131,"title":132,"url":133,"summary":134,"summary_zh":135,"content":9,"source_name":136,"source_url":133,"published_at":137,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":138,"score_detail":139,"sources":142,"tags":144,"primary_topic":147,"secondary_topics":148,"classification":155,"search_phrases":181,"slug":184,"view_count":185,"doi":186,"paper":187,"created_at":219},2320,"Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183121","Geospatial artificial intelligence (GeoAI) integrates multimodal remote sensing with deep learning to model complex agricultural systems at scale. Within this framework, accurate and non-destructive prediction of seed composition from in-season standing crops is essential for breeding and precision agriculture. This study developed an end-to-end convolutional neural network (CNN) framework to estimate eight seed traits (protein, oil, sucrose, fiber, starch, ash, complex and simple carbohydrates) from UAV-based multisensor imagery and associated genotype and phenological metadata. A total of 372 soybean samples were collected over two growing seasons (2020–2021) from two fields in Missouri, with UAV flights capturing multispectral (MSI), thermal (THR), and LiDAR (LDR) data at four time points spanning vegetative to reproductive growth stages. CNN models were trained in single- and multi-date configurations, incorporating genotype (GEN) and days after sowing (DAS) as additional features. The highest accuracy was achieved for sucrose (R2 = 0.80), followed by simple carbohydrate (R2 = 0.70) and starch (R2 = 0.55), with notable gains from GEN and DAS. Multi-date models incorporating earlier acquisitions often matched or outperformed later or all-date combinations. Among modalities, MSI provided the most robust estimates, with limited added value from LDR or THR. Unlike feature-based pipelines prone to multicollinearity, this image-to-trait approach enables automated, scalable prediction of soybean seed composition for in-season, field-level assessment.","地理空间人工智能（GeoAI）将多模态遥感与深度学习相结合，以在大尺度上对复杂农业系统进行建模。在该框架下，对当季未收获作物进行准确且无损的种子成分预测，对于育种和精准农业至关重要。本研究开发了一种端到端的卷积神经网络（CNN）框架，利用基于无人机（UAV）的多传感器影像以及相关的基因型和物候元数据，估算八种种子性状（蛋白质、油分、蔗糖、纤维、淀粉、灰分、复杂碳水化合物和简单碳水化合物）。研究在2020—2021两个生长季内，从密苏里州两块田地共采集372份大豆样本，无人机飞行在从营养生长期到生殖生长期的四个时间点获取了多光谱（MSI）、热红外（THR）和激光雷达（LDR）数据。CNN模型以单日期和多日期配置进行训练，并将基因型（GEN）和播种后天数（DAS）作为附加特征纳入。蔗糖的估算精度最高（R² = 0.80），其次为简单碳水化合物（R² = 0.70）和淀粉（R² = 0.55），且GEN和DAS的加入带来了显著提升。纳入早期采集数据的多日期模型往往与后期或全日期组合的表现相当，甚至更优。在各模态中，MSI提供了最稳健的估算结果，而LDR或THR的附加价值有限。与易受多重共线性影响的特征工程流程不同，这种从图像到性状的方法能够实现大豆种子成分的自动化、可扩展预测，用于当季田块尺度的评估。","Remote Sensing","2026-09-11T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":140,"relevant":21,"comment":141},8,"基于多模态无人机影像与基因型信息的大豆籽粒成分无损预测研究，方法新颖、数据扎实，对智慧育种与精准农业有参考价值。",[143],{"name":136,"url":133},[26,27,145,28,146],"农业人工智能","大豆育种",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[149,154],{"code":150,"slug":151,"name":152,"path":153,"parent_code":49},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":156},{"candidates":157,"agricultural_evidence":58},[158,162,169,174],{"code":39,"score":66,"supported":58,"title":159,"abstract":160,"tags":161},[77],[75,77],[146],{"code":150,"score":13,"supported":58,"title":163,"abstract":164,"tags":168},[],[165,166,167],"深度学习","deep learning","artificial intelligence",[145],{"code":45,"score":13,"supported":58,"title":170,"abstract":171,"tags":173},[],[28,70,172],"multispectral",[28],{"code":88,"score":175,"supported":58,"title":176,"abstract":177,"tags":180},7,[],[178,179],"精准农业","precision agriculture",[26],[182,183],"农业人工智能 大豆育种 智慧农业 无人机","农业人工智能 大豆育种","农业人工智能大豆育种智慧农业无人机-2320",4,"10.3390\u002Frs18183121",{"doi":186,"openalex_id":188,"authors":189,"venue":136,"cited_by_count":119,"oa_url":133,"card":214,"direction":124,"ingested_from":126},"W7212223520",[190,193,196,199,202,204,206,209,212],{"name":191,"orcid":192},"Vasit Sagan","https:\u002F\u002Forcid.org\u002F0000-0003-4375-2096",{"name":194,"orcid":195},"Kristen Rhodes","https:\u002F\u002Forcid.org\u002F0000-0002-8469-5043",{"name":197,"orcid":198},"Sourav Bhadra","https:\u002F\u002Forcid.org\u002F0000-0002-5832-4695",{"name":200,"orcid":201},"Haireti Alifu","https:\u002F\u002Forcid.org\u002F0000-0002-7369-6657",{"name":203,"orcid":9},"Aviskar Giri",{"name":205,"orcid":9},"Ashutosh Pawar",{"name":207,"orcid":208},"Bishal Roy","https:\u002F\u002Forcid.org\u002F0000-0001-9912-5505",{"name":210,"orcid":211},"Supria Sarkar","https:\u002F\u002Forcid.org\u002F0000-0001-9467-5786",{"name":213,"orcid":9},"Felix Fritschi",{"tldr":215,"method":216,"finding":217,"direction":124,"opportunity":218},"用无人机多模态影像与基因型数据训练CNN，预测大豆八种种子成分。","372份大豆样本，多光谱\u002F热红外\u002FLiDAR影像加基因型与播后天数，端到端CNN","蔗糖预测最优(R²=0.80)，基因型与播期提升精度，多光谱最稳健。","可探索基因型-表型-环境交互建模，并迁移至其他作物与多时相早期预测。","2026-09-13T23:30:22.776177Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"content":9,"source_name":226,"source_url":223,"published_at":227,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":228,"score_detail":229,"sources":232,"tags":234,"primary_topic":236,"secondary_topics":237,"classification":240,"search_phrases":282,"slug":285,"view_count":96,"doi":286,"paper":287,"created_at":318},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":17,"substance":18,"depth":17,"authority":230,"freshness":96,"relevant":21,"comment":231},12,"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。",[233],{"name":226,"url":223},[26,145,28,29,235],"牛油果",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[238,239],{"code":150,"slug":151,"name":152,"path":153,"parent_code":49},{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":241},{"candidates":242,"agricultural_evidence":58},[243,247,252,257,264,271,278],{"code":32,"score":17,"supported":58,"title":244,"abstract":245,"tags":246},[33],[62,63,33],[29],{"code":150,"score":230,"supported":58,"title":248,"abstract":250,"tags":251},[249],"machine learning",[167],[145],{"code":45,"score":13,"supported":58,"title":253,"abstract":254,"tags":256},[],[28,255,70,172],"无人机多光谱",[28],{"code":258,"score":175,"supported":58,"title":259,"abstract":260,"tags":263},"B2",[],[261,262],"果树","orchard",[235],{"code":265,"score":81,"supported":58,"title":266,"abstract":267,"tags":270},"H2",[],[268,269],"土壤","soil",[],{"code":272,"score":81,"supported":58,"title":273,"abstract":274,"tags":277},"H7",[],[275,276],"干旱","drought",[],{"code":88,"score":21,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[26],[283,284],"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","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":286,"openalex_id":288,"authors":289,"venue":226,"cited_by_count":119,"oa_url":311,"card":312,"direction":124,"ingested_from":126},"W7211887459",[290,293,296,299,302,305,308],{"name":291,"orcid":292},"Alberto Férez-Gómez","https:\u002F\u002Forcid.org\u002F0000-0002-8545-9237",{"name":294,"orcid":295},"Lucia Soler-Escamez","https:\u002F\u002Forcid.org\u002F0009-0004-1361-6928",{"name":297,"orcid":298},"Cristina Ferrer-Blanco","https:\u002F\u002Forcid.org\u002F0000-0002-3456-917X",{"name":300,"orcid":301},"Adrián Pérez Aguilar","https:\u002F\u002Forcid.org\u002F0000-0001-7012-1551",{"name":303,"orcid":304},"J.I. Hormaza","https:\u002F\u002Forcid.org\u002F0000-0001-5449-7444",{"name":306,"orcid":307},"Almudena Díaz Zayas","https:\u002F\u002Forcid.org\u002F0000-0002-1226-6135",{"name":309,"orcid":310},"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":313,"method":314,"finding":315,"direction":316,"opportunity":317},"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。","智慧农业 \u002F 农业物联网","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",{"id":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":136,"source_url":322,"published_at":325,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":326,"sources":328,"tags":330,"primary_topic":333,"secondary_topics":334,"classification":336,"search_phrases":351,"slug":354,"view_count":96,"doi":355,"paper":356,"created_at":374},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。","2026-09-24T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":327},"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。",[329],{"name":136,"url":322},[26,75,331,28,332],"农情监测","作物表型",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[335],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":337},{"candidates":338,"agricultural_evidence":58},[339,343,347],{"code":39,"score":66,"supported":58,"title":340,"abstract":341,"tags":342},[77],[75,77],[75],{"code":45,"score":20,"supported":58,"title":344,"abstract":345,"tags":346},[],[28,70],[332,331,28],{"code":88,"score":21,"supported":14,"title":348,"abstract":349,"tags":350},[],[],[26],[352,353],"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":355,"openalex_id":357,"authors":358,"venue":136,"cited_by_count":119,"oa_url":322,"card":369,"direction":124,"ingested_from":126},"W7214203102",[359,361,364,367],{"name":360,"orcid":9},"Qiuxiang Yi",{"name":362,"orcid":363},"Siting Chen","https:\u002F\u002Forcid.org\u002F0000-0003-3468-9320",{"name":365,"orcid":366},"Fumin Wang","https:\u002F\u002Forcid.org\u002F0000-0002-5078-358X",{"name":368,"orcid":9},"Qinyan Zhu",{"tldr":370,"method":371,"finding":372,"direction":124,"opportunity":373},"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":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":9,"source_name":381,"source_url":378,"published_at":382,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":390,"tags":392,"primary_topic":396,"secondary_topics":397,"classification":399,"search_phrases":433,"slug":436,"view_count":437,"doi":438,"paper":439,"created_at":450},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",74,{"impact":385,"substance":386,"depth":387,"authority":388,"freshness":140,"relevant":21,"comment":389},16,20,17,13,"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[391],{"name":381,"url":378},[26,393,28,394,395],"农业水资源","灌溉管理","农业干旱",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},[398],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":51,"locked":14,"version":52,"method":53,"evidence":400},{"candidates":401,"agricultural_evidence":58},[402,406,411,415,422,429],{"code":45,"score":66,"supported":58,"title":403,"abstract":404,"tags":405},[70],[28,70],[28],{"code":32,"score":407,"supported":58,"title":408,"abstract":409,"tags":410},11,[],[62,63,30,33,60],[393,394],{"code":272,"score":81,"supported":58,"title":412,"abstract":413,"tags":414},[],[275,276],[],{"code":416,"score":81,"supported":58,"title":417,"abstract":418,"tags":421},"H8",[],[419,420],"可持续农业","sustainable agriculture",[],{"code":423,"score":81,"supported":58,"title":424,"abstract":425,"tags":428},"I7",[],[426,427],"文献计量","bibliometric",[],{"code":88,"score":21,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[26],[434,435],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667",2,"10.1007\u002Fs43621-026-04728-y",{"doi":438,"openalex_id":440,"authors":441,"venue":381,"cited_by_count":119,"oa_url":378,"card":445,"direction":124,"ingested_from":126},"W7213266434",[442],{"name":443,"orcid":444},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":446,"method":447,"finding":448,"direction":124,"opportunity":449},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z",{"id":452,"title":453,"url":454,"summary":455,"summary_zh":456,"content":9,"source_name":457,"source_url":454,"published_at":458,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":464,"primary_topic":465,"secondary_topics":466,"classification":468,"search_phrases":487,"slug":489,"view_count":96,"doi":490,"paper":491,"created_at":513},1444,"An Improved Transformer-KAN Model for Soybean Mapping Based on Multi-Temporal Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16171895","Accurate and transferable soybean mapping is essential for agricultural monitoring and area verification, yet conventional Transformer models can be limited in modeling complex nonlinear phenological relationships and maintaining training stability. This study proposes an improved Transformer-KAN model for multi-temporal Sentinel-2 data. Temporal positional encoding, multi-head self-attention with relative positional bias, and a Pre-LayerNorm residual structure are introduced to strengthen phenological sequence modeling, while FastKAN replaces the conventional MLP-based feed-forward network to enhance nonlinear feature representation. The model was trained using 2023 samples from Hailun City and directly evaluated in Bozhou, McLean, and Cass without retraining or fine-tuning. Cross-year transferability was further evaluated by applying the Hailun-trained model to data from Bozhou and McLean from 2021 to 2025. The proposed model achieved overall accuracies of 0.975, 0.983, 0.958, and 0.966 in Hailun, Bozhou, McLean, and Cass, respectively, with corresponding Kappa coefficients of 0.895, 0.887, 0.883, and 0.916. Complexity analysis showed that Transformer-KAN required 0.8142 M parameters and 1.1967 M FLOPs, with an average inference time of 3.0191 ms per sample, compared with 0.6130 M parameters, 0.7971 M FLOPs, and 1.4009 ms per sample for the conventional Transformer, indicating that the improved feature representation was accompanied by increased computational complexity. Cross-year classification performance remained generally high from 2021 to 2025, although interannual variations were observed due to differences in crop growth conditions, phenological timing, and image acquisition quality. Discrepancies between remote-sensing-derived and officially reported soybean areas were mainly related to differences in statistical definitions and residual classification uncertainties, rather than model transferability. Overall, Transformer-KAN provides accurate and transferable soybean mapping and can serve as a spatially explicit complement to official agricultural statistics.","准确且可迁移的大豆制图对于农业监测和面积核查至关重要，然而传统Transformer模型在建模复杂非线性物候关系及维持训练稳定性方面可能存在局限。本研究提出一种改进的Transformer-KAN模型，用于多时相Sentinel-2数据。引入时间位置编码、带相对位置偏置的多头自注意力机制以及Pre-LayerNorm残差结构，以增强物候序列建模能力，同时采用FastKAN替代传统基于MLP的前馈网络，以提升非线性特征表达能力。该模型利用2023年海伦市样本进行训练，并在北州、麦克莱恩和卡斯直接评估，无需重新训练或微调。跨年度迁移能力进一步通过将海伦训练模型应用于2021至2025年北州和麦克莱恩的数据进行评估。所提模型在海伦、北州、麦克莱恩和卡斯的总体精度分别为0.975、0.983、0.958和0.966，对应的Kappa系数分别为0.895、0.887、0.883和0.916。复杂度分析表明，Transformer-KAN需要0.8142 M参数和1.1967 M FLOPs，平均每样本推理时间为3.0191 ms，而传统Transformer为0.6130 M参数、0.7971 M FLOPs和每样本1.4009 ms，说明改进的特征表示伴随着计算复杂度的增加。2021至2025年跨年度分类性能总体保持较高水平，但由于作物生长条件、物候时序和影像获取质量的差异，存在年际波动。遥感反演大豆面积与官方统计面积之间的差异主要与统计口径差异和残余分类不确定性有关，而非模型迁移能力所致。总体而言，Transformer-KAN能够提供准确且可迁移的大豆制图结果，可作为官方农业统计的空间显式补充。","Agriculture","2026-09-01T00:00:00Z",79,{"impact":17,"substance":18,"depth":386,"authority":230,"freshness":175,"relevant":21,"comment":461},"提出改进的Transformer-KAN模型用于大豆遥感制图，跨区域跨年份验证精度高，对农业监测有实质贡献。",[463],{"name":457,"url":454},[26,145,75,28],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[467],{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":469},{"candidates":470,"agricultural_evidence":58},[471,475,479,483],{"code":39,"score":66,"supported":58,"title":472,"abstract":473,"tags":474},[77],[75,77],[75],{"code":45,"score":230,"supported":58,"title":476,"abstract":477,"tags":478},[70],[28],[28],{"code":88,"score":21,"supported":14,"title":480,"abstract":481,"tags":482},[],[],[26],{"code":150,"score":21,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[145],[488,284],"农业人工智能 智慧农业 大豆 遥感","农业人工智能智慧农业大豆遥感-1444","10.3390\u002Fagriculture16171895",{"doi":490,"openalex_id":492,"authors":493,"venue":457,"cited_by_count":119,"oa_url":454,"card":508,"direction":124,"ingested_from":126},"W7204914742",[494,497,499,502,505],{"name":495,"orcid":496},"Chulin Pan","https:\u002F\u002Forcid.org\u002F0000-0003-3976-465X",{"name":498,"orcid":9},"Jiachen Ju",{"name":500,"orcid":501},"Hongpeng Guo","https:\u002F\u002Forcid.org\u002F0000-0003-0625-8929",{"name":503,"orcid":504},"Yufeng Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-8783-9786",{"name":506,"orcid":507},"Shuang Xu","https:\u002F\u002Forcid.org\u002F0000-0002-3470-0609",{"tldr":509,"method":510,"finding":511,"direction":124,"opportunity":512},"提出改进的Transformer-KAN模型，基于多时相遥感数据实现高精度、可迁移的大豆种植区制图。","结合时间位置编码、相对位置偏置、Pre-LayerNorm和FastKAN替换M","模型在四个区域精度达0.958-0.983，跨年迁移稳定，但计算复杂度高于传统Transformer","可探索降低模型复杂度或轻量化设计，以平衡精度与效率，并研究跨区域物候差异对迁移的影响。","2026-09-02T23:30:53.720188Z",{"id":515,"title":516,"url":517,"summary":518,"summary_zh":519,"content":9,"source_name":520,"source_url":517,"published_at":521,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":522,"score_detail":523,"sources":525,"tags":527,"primary_topic":531,"secondary_topics":532,"classification":539,"search_phrases":565,"slug":568,"view_count":119,"doi":569,"paper":570,"created_at":580},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",75,{"impact":17,"substance":386,"depth":385,"authority":230,"freshness":20,"relevant":21,"comment":524},"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。",[526],{"name":520,"url":517},[145,528,28,529,530],"农业物联网","智慧灌溉","节水农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[533,538],{"code":534,"slug":535,"name":536,"path":537,"parent_code":49},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":45,"slug":46,"name":47,"path":48,"parent_code":49},{"status":51,"locked":14,"version":52,"method":53,"evidence":540},{"candidates":541,"agricultural_evidence":58},[542,546,553,557,561],{"code":32,"score":57,"supported":58,"title":543,"abstract":544,"tags":545},[33],[62,63,33],[529,530],{"code":534,"score":17,"supported":58,"title":547,"abstract":549,"tags":552},[548],"IoT",[550,551,548],"物联网","internet of things",[528],{"code":45,"score":66,"supported":58,"title":554,"abstract":555,"tags":556},[70],[28,70],[28],{"code":150,"score":230,"supported":58,"title":558,"abstract":559,"tags":560},[167],[167],[145],{"code":416,"score":81,"supported":58,"title":562,"abstract":563,"tags":564},[],[419,420],[],[566,567],"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":569,"openalex_id":571,"authors":572,"venue":520,"cited_by_count":119,"oa_url":517,"card":575,"direction":316,"ingested_from":126},"W7223090389",[573],{"name":574,"orcid":9},"Patil",{"tldr":576,"method":577,"finding":578,"direction":316,"opportunity":579},"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"]