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Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z","论文",false,65,{"impact":416,"substance":361,"depth":584,"authority":585,"freshness":385,"relevant":455,"comment":586},16,9,"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[588],{"name":578,"url":575},[590,591,592,593,594],"智慧农业","农业人工智能","无损检测","多光谱成像","食品质量检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[597],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":601},"classified","rules",{"candidates":602,"agricultural_evidence":604},[603,609,620,627,633,639,645,651],{"code":32,"score":416,"supported":604,"title":605,"abstract":607,"tags":608},true,[606],"multispectral",[606],[593],{"code":24,"score":385,"supported":604,"title":610,"abstract":611,"tags":619},[],[612,613,614,615,616,617,618],"深度学习","机器学习","计算机视觉","machine learning","deep learning","artificial intelligence","computer vision",[591],{"code":465,"score":621,"supported":604,"title":622,"abstract":623,"tags":626},7,[],[624,625],"食品安全","food safety",[592],{"code":208,"score":628,"supported":604,"title":629,"abstract":630,"tags":632},6,[],[631,209],"稻米",[],{"code":279,"score":628,"supported":604,"title":634,"abstract":635,"tags":638},[],[636,637],"番茄","tomato",[],{"code":286,"score":628,"supported":604,"title":640,"abstract":641,"tags":644},[],[642,643],"苹果","apple",[],{"code":442,"score":646,"supported":604,"title":647,"abstract":648,"tags":650},3,[],[649],"农产品加工",[],{"code":16,"score":455,"supported":581,"title":652,"abstract":653,"tags":654},[],[],[590],[656,657],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":659,"openalex_id":661,"authors":662,"venue":578,"cited_by_count":424,"oa_url":575,"card":675,"direction":681,"ingested_from":682},"W7223139905",[663,665,667,669,671,673],{"name":664,"orcid":11},"Eshwari Take",{"name":666,"orcid":11},"Avanish Raut",{"name":668,"orcid":11},"Vedant Joshi",{"name":670,"orcid":11},"Shravani Akolkar",{"name":672,"orcid":11},"R. M. Gharat",{"name":674,"orcid":11},"Shruti Dive",{"tldr":676,"method":677,"finding":678,"direction":679,"opportunity":680},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","openalex","2026-10-22T11:20:48.684398Z",{"id":685,"title":686,"url":687,"summary":688,"summary_zh":689,"content":11,"source_name":690,"source_url":687,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":691,"score_detail":692,"sources":694,"tags":696,"primary_topic":700,"secondary_topics":701,"classification":704,"search_phrases":736,"slug":739,"view_count":424,"doi":740,"paper":741,"created_at":768},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy",80,{"impact":584,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":693},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[695],{"name":690,"url":687},[590,591,697,698,699],"农业遥感","氮素监测","紫花苜蓿",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[702,703],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":364,"slug":365,"name":366,"path":367,"parent_code":332},{"status":599,"locked":581,"version":4,"method":600,"evidence":705},{"candidates":706,"agricultural_evidence":604},[707,714,721,727,732],{"code":32,"score":361,"supported":604,"title":708,"abstract":710,"tags":713},[709],"hyperspectral",[711,712,709],"遥感","remote sensing",[697],{"code":256,"score":715,"supported":604,"title":716,"abstract":718,"tags":720},15,[717],"alfalfa",[719,717],"苜蓿",[699],{"code":364,"score":385,"supported":604,"title":722,"abstract":723,"tags":726},[],[724,719,725],"饲草","forage",[699],{"code":24,"score":385,"supported":604,"title":728,"abstract":729,"tags":731},[],[612,613,615,616,730],"decision support",[591],{"code":16,"score":455,"supported":581,"title":733,"abstract":734,"tags":735},[],[],[590],[737,738],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":740,"openalex_id":742,"authors":743,"venue":690,"cited_by_count":424,"oa_url":687,"card":762,"direction":766,"ingested_from":682},"W7223154576",[744,746,748,750,752,755,757,760],{"name":745,"orcid":11},"Shuai Fu",{"name":747,"orcid":11},"Yunhao Li",{"name":749,"orcid":11},"Chunli Miao",{"name":751,"orcid":11},"Jie Liu",{"name":753,"orcid":754},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":756,"orcid":11},"Yongjia Hu",{"name":758,"orcid":759},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":761,"orcid":11},"吴彩霞",{"tldr":763,"method":764,"finding":765,"direction":766,"opportunity":767},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","农业遥感与作物表型","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":770,"title":771,"url":772,"summary":773,"summary_zh":774,"content":11,"source_name":775,"source_url":772,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":776,"score_detail":777,"sources":780,"tags":782,"primary_topic":785,"secondary_topics":786,"classification":787,"search_phrases":804,"slug":807,"view_count":424,"doi":808,"paper":809,"created_at":828},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy",73,{"impact":416,"substance":508,"depth":778,"authority":345,"freshness":385,"relevant":455,"comment":779},17,"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[781],{"name":775,"url":772},[590,783,711,784],"作物制图","SAR",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":788},{"candidates":789,"agricultural_evidence":604},[790,794,800],{"code":32,"score":385,"supported":604,"title":791,"abstract":792,"tags":793},[],[711,712,606],[711],{"code":263,"score":628,"supported":604,"title":795,"abstract":796,"tags":799},[],[797,798],"粮食安全","food security",[],{"code":16,"score":455,"supported":581,"title":801,"abstract":802,"tags":803},[],[],[590],[805,806],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":808,"openalex_id":810,"authors":811,"venue":775,"cited_by_count":424,"oa_url":772,"card":823,"direction":766,"ingested_from":682},"W7223117203",[812,815,817,820],{"name":813,"orcid":814},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":816,"orcid":11},"Wentao Lv",{"name":818,"orcid":819},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":821,"orcid":822},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":824,"method":825,"finding":826,"direction":766,"opportunity":827},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":830,"title":831,"url":832,"summary":833,"summary_zh":834,"content":11,"source_name":835,"source_url":832,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":329,"score_detail":836,"sources":838,"tags":840,"primary_topic":843,"secondary_topics":844,"classification":846,"search_phrases":861,"slug":864,"view_count":424,"doi":865,"paper":866,"created_at":876},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。","Zenodo (CERN European Organization for Nuclear Research)",{"impact":173,"substance":508,"depth":778,"authority":416,"freshness":385,"relevant":455,"comment":837},"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。",[839],{"name":835,"url":832},[590,591,841,711,842],"产量预测","可解释性",{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},[845],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":847},{"candidates":848,"agricultural_evidence":604},[849,853,857],{"code":24,"score":416,"supported":604,"title":850,"abstract":851,"tags":852},[618],[614],[591],{"code":32,"score":173,"supported":604,"title":854,"abstract":855,"tags":856},[],[711,712],[841,711],{"code":16,"score":455,"supported":581,"title":858,"abstract":859,"tags":860},[],[],[590],[862,863],"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":865,"openalex_id":867,"authors":868,"venue":835,"cited_by_count":424,"oa_url":832,"card":871,"direction":766,"ingested_from":682},"W7223114002",[869],{"name":870,"orcid":11},"Kadiatou Barry",{"tldr":872,"method":873,"finding":874,"direction":766,"opportunity":875},"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":878,"title":879,"url":880,"summary":881,"summary_zh":882,"content":11,"source_name":883,"source_url":880,"published_at":884,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":885,"score_detail":886,"sources":888,"tags":890,"primary_topic":893,"secondary_topics":894,"classification":896,"search_phrases":917,"slug":920,"view_count":424,"doi":921,"paper":922,"created_at":964},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":585,"relevant":455,"comment":887},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[889],{"name":883,"url":880},[590,891,892,711,699],"无人机","育种",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[895],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":897},{"candidates":898,"agricultural_evidence":604},[899,903,907,913],{"code":256,"score":715,"supported":604,"title":900,"abstract":901,"tags":902},[717],[719,717],[699],{"code":32,"score":416,"supported":604,"title":904,"abstract":905,"tags":906},[606],[606],[711],{"code":16,"score":621,"supported":604,"title":908,"abstract":909,"tags":912},[],[910,911],"精准农业","precision agriculture",[590],{"code":364,"score":400,"supported":604,"title":914,"abstract":915,"tags":916},[],[719],[699],[918,919],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":921,"openalex_id":923,"authors":924,"venue":883,"cited_by_count":424,"oa_url":880,"card":959,"direction":766,"ingested_from":682},"W7223083966",[925,928,931,934,937,939,941,944,947,950,953,956],{"name":926,"orcid":927},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":929,"orcid":930},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":932,"orcid":933},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":935,"orcid":936},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":938,"orcid":11},"Julie Hansen",{"name":940,"orcid":11},"Christopher Pierce",{"name":942,"orcid":943},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":945,"orcid":946},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":948,"orcid":949},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":951,"orcid":952},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":954,"orcid":955},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":957,"orcid":958},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":960,"method":961,"finding":962,"direction":766,"opportunity":963},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z",{"id":966,"title":967,"url":968,"summary":969,"summary_zh":970,"content":11,"source_name":835,"source_url":968,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":971,"score_detail":972,"sources":974,"tags":976,"primary_topic":980,"secondary_topics":981,"classification":984,"search_phrases":1015,"slug":1018,"view_count":424,"doi":1019,"paper":1020,"created_at":1031},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":361,"substance":508,"depth":584,"authority":416,"freshness":585,"relevant":455,"comment":973},"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。",[975],{"name":835,"url":968},[591,977,711,978,979],"农业物联网","智慧灌溉","节水农业",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[982,983],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":985},{"candidates":986,"agricultural_evidence":604},[987,994,1001,1005,1009],{"code":152,"score":988,"supported":604,"title":989,"abstract":990,"tags":993},19,[153],[991,992,153],"灌溉","水资源",[978,979],{"code":48,"score":361,"supported":604,"title":995,"abstract":997,"tags":1000},[996],"IoT",[998,999,996],"物联网","internet of things",[977],{"code":32,"score":715,"supported":604,"title":1002,"abstract":1003,"tags":1004},[712],[711,712],[711],{"code":24,"score":416,"supported":604,"title":1006,"abstract":1007,"tags":1008},[617],[617],[591],{"code":192,"score":628,"supported":604,"title":1010,"abstract":1011,"tags":1014},[],[1012,1013],"可持续农业","sustainable agriculture",[],[1016,1017],"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":1019,"openalex_id":1021,"authors":1022,"venue":835,"cited_by_count":424,"oa_url":968,"card":1025,"direction":1029,"ingested_from":682},"W7223090389",[1023],{"name":1024,"orcid":11},"Patil",{"tldr":1026,"method":1027,"finding":1028,"direction":1029,"opportunity":1030},"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。","智慧农业 \u002F 农业物联网","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":1033,"title":1034,"url":1035,"summary":1036,"summary_zh":11,"content":11,"source_name":1037,"source_url":11,"published_at":579,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1038,"score_detail":1039,"sources":1041,"tags":1043,"primary_topic":1047,"secondary_topics":1048,"classification":1051,"search_phrases":1071,"slug":1074,"view_count":455,"doi":11,"paper":1075,"created_at":1083},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21",82,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":1040},"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[1042],{"name":1037,"url":1035},[590,697,1044,1045,1046],"作物表型","无人机多光谱","小麦渍害",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1049,1050],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1052},{"candidates":1053,"agricultural_evidence":604},[1054,1058,1062,1067],{"code":216,"score":524,"supported":604,"title":1055,"abstract":1056,"tags":1057},[218],[218],[],{"code":32,"score":524,"supported":604,"title":1059,"abstract":1060,"tags":1061},[1045],[],[1044,697,1045],{"code":184,"score":173,"supported":604,"title":1063,"abstract":1065,"tags":1066},[1064],"渍害",[],[],{"code":16,"score":455,"supported":581,"title":1068,"abstract":1069,"tags":1070},[],[],[590],[1072,1073],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":11,"openalex_id":11,"authors":1076,"venue":11,"cited_by_count":424,"oa_url":11,"card":1077,"direction":766,"ingested_from":1082},[],{"tldr":1078,"method":1079,"finding":1080,"direction":766,"opportunity":1081},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":1085,"title":1086,"url":1087,"summary":1088,"summary_zh":1089,"content":11,"source_name":1090,"source_url":1087,"published_at":1091,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1092,"score_detail":1093,"sources":1096,"tags":1098,"primary_topic":1103,"secondary_topics":1104,"classification":1105,"search_phrases":1135,"slug":1138,"view_count":424,"doi":1139,"paper":1140,"created_at":1156},5395,"Strengthening Flood Resilience in Data-Sparse Agricultural Regions: A Planning Approach for Coastal Guyana","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23451582","Abstract. Coastal Guyana concentrates most of the country’s population and agricultural production in low-lying polder landscapes that are increasingly exposed to flooding from intense rainfall, sea-level rise, and deterioration of drainage and irrigation infrastructure. Engineering design and resilience planning in these settings are often constrained by limited hydrometric monitoring (discharge, water levels), incomplete documented drainage network, and restricted access to institutional datasets. This paper presents the value of using a low-information framework for flood resilience planning and preliminary hydraulic design in data-sparse agricultural regions, using the 364 km² part of Guyana’s Mahaica–Mahaicony catchment as a case study. The framework integrates (i) rainfall frequency analysis from long-term gauge records, (ii) remote sensing and global datasets (DEM and land-use\u002Fland-cover) to delineate sub-basins and screen flood impact, (iii) simplified runoff and volume-based drainage calculations consistent with rice-field storage and saturated soils, and (iv) structured stakeholder engagement to identify flood-prone areas, operational constraints, and feasible interventions. Six-sub-basins are used for developing a routing strategy to distribute runoff volumes and to explicitly incorporate upstream detention in the wetlands, thereby reducing peak flows conveyed northward through constrained canal corridors. High-level drainage canals, dedicated conveyance paths for critical sub-basins, and dual-purpose drains supporting both drainage and irrigation storage are used to determine preliminary cross-section designs, which are then assessed with 1D hydraulic modelling under tidal boundary conditions. The approach supports phased investment planning that improves flood safety, protects livelihoods, and increases agricultural reliability while aligning with national adaptation priorities.","摘要：圭亚那沿海地区集中了全国大部分人口和农业生产，地处低洼圩田景观，日益面临强降雨、海平面上升以及排水灌溉基础设施老化所带来的洪涝风险。在此类地区，工程设计与韧性规划往往受限于水文监测（流量、水位）资料不足、排水管网记录不完整以及机构数据集获取受限。本文以圭亚那马海卡—马海科尼（Mahaica–Mahaicony）流域364 km²的区域为案例，展示了在数据稀疏的农业地区采用低信息框架进行洪涝韧性规划与初步水力设计的价值。该框架整合了：(i) 基于长期测站记录的降雨频率分析；(ii) 利用遥感与全球数据集（DEM及土地利用\u002F土地覆盖）划分子流域并筛查洪涝影响；(iii) 与稻田蓄水和饱和土壤条件相适应的简化径流与基于水量的排水计算；(iv) 结构化利益相关方参与，以识别易涝区域、运行约束和可行干预措施。研究将流域划分为六个子流域，用于制定汇流路径策略，以分配径流量并明确纳入上游湿地滞蓄，从而削减通过受限渠廊向北输送的峰值流量。基于高位排水渠、关键子流域专用输水通道以及兼顾排水与灌溉蓄水的两用沟渠，确定初步断面设计，并在潮汐边界条件下采用一维水动力模型进行评估。该方法支持分阶段投资规划，在提升防洪安全、保护生计和提高农业可靠性的同时，与国家适应优先事项相衔接。","Geo-Eco-Marina","2026-10-18T00:00:00Z",70,{"impact":416,"substance":508,"depth":778,"authority":1094,"freshness":173,"relevant":455,"comment":1095},13,"该论文提出一种低信息条件下的防洪韧性规划框架，方法新颖，但案例位于圭亚那，对我国三农信息化的直接参考价值有限，因此不宜进入每日精选。",[1097],{"name":1090,"url":1087},[1099,711,1100,1101,1102],"数字乡村","气候适应","防洪韧性","灌排设施",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1106},{"candidates":1107,"agricultural_evidence":604},[1108,1112,1117,1121,1126,1131],{"code":32,"score":621,"supported":604,"title":1109,"abstract":1110,"tags":1111},[],[711,712],[711],{"code":208,"score":628,"supported":604,"title":1113,"abstract":1114,"tags":1116},[],[1115,209],"稻田",[],{"code":152,"score":628,"supported":604,"title":1118,"abstract":1119,"tags":1120},[],[991,153],[],{"code":184,"score":400,"supported":604,"title":1122,"abstract":1123,"tags":1125},[],[1124],"洪涝",[1100],{"code":144,"score":646,"supported":604,"title":1127,"abstract":1128,"tags":1130},[],[1129],"土壤",[],{"code":56,"score":455,"supported":581,"title":1132,"abstract":1133,"tags":1134},[],[],[1099],[1136,1137],"Guyana Mahaica Mahaicony 洪水","沿海稻田 排水规划","GuyanaMahaicaMahaicony洪水-5395","10.5281\u002Fzenodo.23451582",{"doi":1139,"openalex_id":1141,"authors":1142,"venue":1090,"cited_by_count":424,"oa_url":1087,"card":1150,"direction":766,"ingested_from":682},"W7222952224",[1143,1145,1147],{"name":1144,"orcid":11},"Omesh Persaud",{"name":1146,"orcid":11},"Colis Allen",{"name":1148,"orcid":1149},"Ioana Popescu","https:\u002F\u002Forcid.org\u002F0000-0002-1009-5424",{"tldr":1151,"method":1152,"finding":1153,"direction":1154,"opportunity":1155},"提出低信息框架，为数据稀缺农业区规划洪水韧性并做初步水力设计。","降雨频率分析、遥感DEM与土地利用、简化径流计算、利益相关者参与及一维水力模型。","上游湿地滞蓄与分区排水可降低峰值流量，支撑分阶段投资规划。","农业绿色发展与碳","可探索低监测区遥感与参与式数据融合的洪水韧性规划方法及作物淹水风险量化。","2026-10-20T23:30:19.345049Z",{"id":1158,"title":1159,"url":1160,"summary":1161,"summary_zh":1162,"content":11,"source_name":1163,"source_url":1160,"published_at":1091,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1164,"score_detail":1165,"sources":1167,"tags":1169,"primary_topic":1170,"secondary_topics":1171,"classification":1173,"search_phrases":1201,"slug":1204,"view_count":424,"doi":1205,"paper":1206,"created_at":1219},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering",78,{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":173,"relevant":455,"comment":1166},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[1168],{"name":1163,"url":1160},[590,591,210,841,711],{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1172],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1174},{"candidates":1175,"agricultural_evidence":604},[1176,1180,1184,1188,1193,1197],{"code":208,"score":715,"supported":604,"title":1177,"abstract":1178,"tags":1179},[209],[210,209],[210],{"code":32,"score":173,"supported":604,"title":1181,"abstract":1182,"tags":1183},[],[711,712],[841,711],{"code":263,"score":628,"supported":604,"title":1185,"abstract":1186,"tags":1187},[],[797,798],[],{"code":542,"score":646,"supported":604,"title":1189,"abstract":1190,"tags":1192},[],[1191],"农业规划",[],{"code":16,"score":455,"supported":581,"title":1194,"abstract":1195,"tags":1196},[],[],[590],{"code":24,"score":455,"supported":581,"title":1198,"abstract":1199,"tags":1200},[],[],[591],[1202,1203],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":1205,"openalex_id":1207,"authors":1208,"venue":1163,"cited_by_count":424,"oa_url":1160,"card":1214,"direction":766,"ingested_from":682},"W7222942064",[1209,1211],{"name":1210,"orcid":11},"Nayana Venkatesh",{"name":1212,"orcid":1213},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":1215,"method":1216,"finding":1217,"direction":766,"opportunity":1218},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":1221,"title":1222,"url":1223,"summary":1224,"summary_zh":1225,"content":11,"source_name":1226,"source_url":1223,"published_at":1227,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1228,"score_detail":1229,"sources":1231,"tags":1233,"primary_topic":1238,"secondary_topics":1239,"classification":1240,"search_phrases":1261,"slug":1264,"view_count":424,"doi":1265,"paper":1266,"created_at":1280},5391,"Spatio-temporal analysis and forecasting of land use\u002Fland cover change in East Medinipur district, India using CA-Markov model","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fgac.2026.23669","Land Use\u002FLand Cover (LU\u002FLC) serve as essential indicators of global environmental changes. This study examined the LU\u002FLC change in the East Medinipur district of West Bengal, India, using remote sensing data and the CA-Markov modeling approach. Through GIS tools and satellite imagery, the study maps, monitors, and analyzes LU\u002FLC changes over a 20-year period (2001–2021), utilizing data from Landsat 4–5 and Landsat 8. Analytical tools including ArcGIS 9, IDRISI Terset, and ERDAS 2014 are employed to project LU\u002FLC trends up to 2041. The results highlight significant transformations, notable reductions in agriculture, vegetation, barren land, and water bodies, alongside substantial increases in aquaculture and built-up areas from 2001 to 2021. Projections indicates that these trends will continue through 2041, with further declines in agriculture, vegetation, barren land, and water bodies, accompanied by continued growth in aquaculture and built-up areas.","土地利用\u002F土地覆盖（Land Use\u002FLand Cover，LU\u002FLC）是全球环境变化的重要指标。本研究利用遥感数据和CA-Markov建模方法，考察了印度西孟加拉邦东梅迪尼普尔县的土地利用\u002F土地覆盖变化。通过GIS工具和卫星影像，研究利用Landsat 4–5和Landsat 8数据，对2001—2021年20年间的土地利用\u002F土地覆盖变化进行了制图、监测与分析。研究采用ArcGIS 9、IDRISI Terset和ERDAS 2014等分析工具，对至2041年的土地利用\u002F土地覆盖趋势进行预测。结果表明，2001年至2021年间发生了显著转变，农业、植被、裸地和水体明显减少，水产养殖和建成区则大幅增加。预测显示，这些趋势将持续至2041年，农业、植被、裸地和水体进一步减少，水产养殖和建成区持续增长。","Geodesy and Cartography","2026-10-19T00:00:00Z",61,{"impact":173,"substance":361,"depth":715,"authority":416,"freshness":173,"relevant":455,"comment":1230},"该研究基于遥感与CA-Markov模型分析印度区域土地利用变化，方法规范、结论明确，但属区域性案例，对国内三农与农业信息化的直接参考价值有限，可作为遥感应用类论文收录，不宜进入每日精选。",[1232],{"name":1226,"url":1223},[1234,711,1235,1236,1237],"水产养殖","土地利用","耕地变化","CA-Markov",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1241},{"candidates":1242,"agricultural_evidence":604},[1243,1248,1252,1257],{"code":388,"score":621,"supported":604,"title":1244,"abstract":1245,"tags":1247},[],[1246,389],"水产",[1234],{"code":32,"score":621,"supported":604,"title":1249,"abstract":1250,"tags":1251},[],[711,712],[711],{"code":40,"score":646,"supported":604,"title":1253,"abstract":1254,"tags":1256},[],[1255],"GIS",[],{"code":176,"score":455,"supported":581,"title":1258,"abstract":1259,"tags":1260},[],[],[1235],[1262,1263],"Landsat 土地利用 遥感监测","土地利用 水产养殖 耕地变化 遥感","Landsat土地利用遥感监测-5391","10.3846\u002Fgac.2026.23669",{"doi":1265,"openalex_id":1267,"authors":1268,"venue":1226,"cited_by_count":424,"oa_url":1274,"card":1275,"direction":766,"ingested_from":682},"W7222963948",[1269,1272],{"name":1270,"orcid":1271},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":1273,"orcid":11},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":1276,"method":1277,"finding":1278,"direction":766,"opportunity":1279},"用遥感与CA-Markov模型分析印度东梅迪尼普尔2001-2021年土地利用变化并预测至2041年","Landsat影像、GIS与IDRISI\u002FERDAS，CA-Markov模型做L","农业、植被、裸地和水体持续减少，水产养殖与建设用地显著扩张并将延续至2041年。","可将CA-Markov与作物适宜性\u002F粮食安全模型耦合，评估养殖与建设用地扩张对农业生产的长期威胁。","2026-10-20T23:30:19.057951Z",{"id":1282,"title":1283,"url":1284,"summary":1285,"summary_zh":1286,"content":11,"source_name":1287,"source_url":1284,"published_at":1227,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":1288,"sources":1290,"tags":1292,"primary_topic":1296,"secondary_topics":1297,"classification":1298,"search_phrases":1318,"slug":1321,"view_count":424,"doi":1322,"paper":1323,"created_at":1351},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":1289},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[1291],{"name":1287,"url":1284},[1293,1235,1294,1295],"遥感监测","土壤侵蚀","乡村人口",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1299},{"candidates":1300,"agricultural_evidence":604},[1301,1305,1309,1314],{"code":32,"score":621,"supported":604,"title":1302,"abstract":1303,"tags":1304},[],[711,712],[1293],{"code":40,"score":646,"supported":604,"title":1306,"abstract":1307,"tags":1308},[],[1255],[],{"code":64,"score":646,"supported":604,"title":1310,"abstract":1311,"tags":1313},[],[1312],"methodological framework",[],{"code":176,"score":455,"supported":581,"title":1315,"abstract":1316,"tags":1317},[],[],[1235],[1319,1320],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":1322,"openalex_id":1324,"authors":1325,"venue":1287,"cited_by_count":424,"oa_url":1284,"card":1345,"direction":766,"ingested_from":682},"W7222945959",[1326,1329,1332,1334,1337,1340,1343],{"name":1327,"orcid":1328},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":1330,"orcid":1331},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":1333,"orcid":11},"Милан Радовић",{"name":1335,"orcid":1336},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":1338,"orcid":1339},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":1341,"orcid":1342},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":1344,"orcid":11},"Manojlović Stefan",{"tldr":1346,"method":1347,"finding":1348,"direction":1349,"opportunity":1350},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z",{"id":1353,"title":1354,"url":1355,"summary":1356,"summary_zh":1357,"content":11,"source_name":1358,"source_url":1355,"published_at":1359,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1360,"score_detail":1361,"sources":1363,"tags":1365,"primary_topic":1368,"secondary_topics":1369,"classification":1371,"search_phrases":1392,"slug":1395,"view_count":424,"doi":1396,"paper":1397,"created_at":1420},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":584,"substance":321,"depth":361,"authority":1094,"freshness":173,"relevant":455,"comment":1362},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[1364],{"name":1358,"url":1355},[590,891,1293,1366,1367],"冬小麦","生物量估算",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1370],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1372},{"candidates":1373,"agricultural_evidence":604},[1374,1380,1384,1388],{"code":216,"score":1375,"supported":604,"title":1376,"abstract":1378,"tags":1379},26,[1377,217],"winter wheat",[218,1377,217],[1366],{"code":32,"score":584,"supported":604,"title":1381,"abstract":1382,"tags":1383},[712],[711,712],[1367,1293],{"code":24,"score":628,"supported":604,"title":1385,"abstract":1386,"tags":1387},[],[613,615],[],{"code":16,"score":455,"supported":581,"title":1389,"abstract":1390,"tags":1391},[],[],[590],[1393,1394],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":1396,"openalex_id":1398,"authors":1399,"venue":1358,"cited_by_count":424,"oa_url":1355,"card":1415,"direction":766,"ingested_from":682},"W7222806066",[1400,1402,1404,1406,1409,1411,1413],{"name":1401,"orcid":11},"Guangtao Wang",{"name":1403,"orcid":11},"Sumei Yao",{"name":1405,"orcid":11},"Yanzheng Xie",{"name":1407,"orcid":1408},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":1410,"orcid":11},"Xiangdong Chen",{"name":1412,"orcid":11},"Yuhao Zhang",{"name":1414,"orcid":11},"Lina Jiang",{"tldr":1416,"method":1417,"finding":1418,"direction":766,"opportunity":1419},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":1422,"title":1423,"url":1424,"summary":1425,"summary_zh":1426,"content":11,"source_name":1427,"source_url":1424,"published_at":1359,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":1428,"sources":1430,"tags":1432,"primary_topic":1436,"secondary_topics":1437,"classification":1440,"search_phrases":1458,"slug":1461,"view_count":424,"doi":1462,"paper":1463,"created_at":1475},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":1429},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[1431],{"name":1427,"url":1424},[1433,1293,1434,1435],"防灾减灾","洪水风险评估","GIS应用",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[1438,1439],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"status":599,"locked":581,"version":4,"method":600,"evidence":1441},{"candidates":1442,"agricultural_evidence":604},[1443,1447,1452],{"code":32,"score":715,"supported":604,"title":1444,"abstract":1445,"tags":1446},[712],[711,712],[1293],{"code":40,"score":345,"supported":604,"title":1448,"abstract":1449,"tags":1451},[1255],[1450,1255],"地理信息",[],{"code":184,"score":173,"supported":604,"title":1453,"abstract":1454,"tags":1457},[],[1455,1456],"干旱","drought",[1434,1433],[1459,1460],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":1462,"openalex_id":1464,"authors":1465,"venue":1427,"cited_by_count":424,"oa_url":1469,"card":1470,"direction":766,"ingested_from":682},"W7222885988",[1466],{"name":1467,"orcid":1468},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":1471,"method":1472,"finding":1473,"direction":766,"opportunity":1474},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":1477,"title":1478,"url":1479,"summary":1480,"summary_zh":1481,"content":11,"source_name":1482,"source_url":1479,"published_at":1091,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1483,"score_detail":1484,"sources":1486,"tags":1488,"primary_topic":1492,"secondary_topics":1493,"classification":1495,"search_phrases":1521,"slug":1524,"view_count":424,"doi":1525,"paper":1526,"created_at":1544},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex",60,{"impact":173,"substance":361,"depth":715,"authority":385,"freshness":585,"relevant":455,"comment":1485},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[1487],{"name":1482,"url":1479},[711,1255,1489,1490,1491],"农用地适宜性","土地覆被","孟加拉农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":7},[1494],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1496},{"candidates":1497,"agricultural_evidence":604},[1498,1503,1507,1511,1517],{"code":40,"score":361,"supported":604,"title":1499,"abstract":1500,"tags":1502},[1255],[1450,1255,1501],"geographic information",[1255],{"code":32,"score":715,"supported":604,"title":1504,"abstract":1505,"tags":1506},[712],[711,712],[711],{"code":208,"score":628,"supported":604,"title":1508,"abstract":1509,"tags":1510},[],[631,209],[],{"code":480,"score":628,"supported":604,"title":1512,"abstract":1513,"tags":1516},[],[1514,1515],"农村发展","rural development",[],{"code":144,"score":628,"supported":604,"title":1518,"abstract":1519,"tags":1520},[],[1129,145],[],[1522,1523],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":1525,"openalex_id":1527,"authors":1528,"venue":1538,"cited_by_count":424,"oa_url":1479,"card":1539,"direction":766,"ingested_from":682},"W7222895468",[1529,1531,1533,1535],{"name":1530,"orcid":11},"Raju Ahmed",{"name":1532,"orcid":11},"Shaikh Mehdee Mohammad",{"name":1534,"orcid":11},"Ummul Momanin Coalee",{"name":1536,"orcid":1537},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":1540,"method":1541,"finding":1542,"direction":766,"opportunity":1543},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z",{"id":1546,"title":1547,"url":1548,"summary":1549,"summary_zh":1550,"content":11,"source_name":1551,"source_url":1548,"published_at":1227,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1552,"score_detail":1553,"sources":1555,"tags":1557,"primary_topic":1562,"secondary_topics":1563,"classification":1564,"search_phrases":1575,"slug":1578,"view_count":424,"doi":1579,"paper":1580,"created_at":1611},5318,"Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10668-026-08214-3","Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China。Environment Development and Sustainability","中央环保督察政策对农业秸秆焚烧的滞后效应与作用机制：来自中国遥感数据的证据。《环境、发展与可持续性》","Environment Development and Sustainability",84,{"impact":321,"substance":508,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":1554},"基于全国遥感数据评估中央环保督察对秸秆焚烧的滞后效应，数据规模大、方法新颖，对农业环境政策有参考价值，值得进入每日精选。",[1556],{"name":1551,"url":1548},[1558,1293,1559,1560,1561],"农业政策","卫星遥感","秸秆焚烧","环保督察",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1565},{"candidates":1566,"agricultural_evidence":604},[1567,1571],{"code":32,"score":584,"supported":604,"title":1568,"abstract":1569,"tags":1570},[712],[711,712],[1559,1293],{"code":168,"score":455,"supported":581,"title":1572,"abstract":1573,"tags":1574},[],[],[1560],[1576,1577],"中央环保督察 秸秆焚烧","遥感数据 秸秆焚烧","中央环保督察秸秆焚烧-5318","10.1007\u002Fs10668-026-08214-3",{"doi":1579,"openalex_id":1581,"authors":1582,"venue":1551,"cited_by_count":424,"oa_url":11,"card":1606,"direction":766,"ingested_from":682},"W7222877438",[1583,1585,1587,1589,1591,1593,1595,1598,1600,1602,1604],{"name":1584,"orcid":11},"Lu Dong",{"name":1586,"orcid":11},"Linru Zhang",{"name":1588,"orcid":11},"Na Liu",{"name":1590,"orcid":11},"Xin Long",{"name":1592,"orcid":11},"Ziyi Wang",{"name":1594,"orcid":11},"Siyue Yang",{"name":1596,"orcid":1597},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":1599,"orcid":11},"Qiyuan Wang",{"name":1601,"orcid":11},"Zhanfeng Dong",{"name":1603,"orcid":11},"Long Wu",{"name":1605,"orcid":11},"Yichen Wang",{"tldr":1607,"method":1608,"finding":1609,"direction":1154,"opportunity":1610},"利用遥感数据评估中央环保督察政策对秸秆焚烧的滞后效应与作用机制。","基于中国遥感火点数据构建面板，采用滞后效应模型识别政策影响机制。","中央环保督察对秸秆焚烧具有显著但滞后的抑制效应，并存在区域异质性。","可结合地块级遥感与农户调查，量化督察滞后期内秸秆离田利用与替代处置的路径。","2026-10-19T23:30:22.693590Z",{"id":1613,"title":1614,"url":1615,"summary":1616,"summary_zh":11,"content":11,"source_name":1617,"source_url":11,"published_at":1618,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1619,"score_detail":1620,"sources":1623,"tags":1625,"primary_topic":1626,"secondary_topics":1627,"classification":1630,"search_phrases":1649,"slug":1652,"view_count":424,"doi":11,"paper":1653,"created_at":1660},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",76,{"impact":584,"substance":1621,"depth":778,"authority":345,"freshness":173,"relevant":455,"comment":1622},21,"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[1624],{"name":1617,"url":1615},[590,697,1044,1045,1046],{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[1628,1629],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1631},{"candidates":1632,"agricultural_evidence":604},[1633,1637,1641,1645],{"code":216,"score":524,"supported":604,"title":1634,"abstract":1635,"tags":1636},[218],[218],[],{"code":184,"score":524,"supported":604,"title":1638,"abstract":1639,"tags":1640},[1064],[1064],[],{"code":32,"score":524,"supported":604,"title":1642,"abstract":1643,"tags":1644},[1045],[],[1044,697,1045],{"code":16,"score":400,"supported":604,"title":1646,"abstract":1647,"tags":1648},[],[590],[590],[1650,1651],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":11,"openalex_id":11,"authors":1654,"venue":11,"cited_by_count":424,"oa_url":11,"card":1655,"direction":766,"ingested_from":1082},[],{"tldr":1656,"method":1657,"finding":1658,"direction":766,"opportunity":1659},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":1662,"title":1663,"url":1664,"summary":1665,"summary_zh":1666,"content":11,"source_name":1667,"source_url":1664,"published_at":1359,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":424,"score_detail":1668,"sources":1670,"tags":1672,"primary_topic":1676,"secondary_topics":1677,"classification":1678,"search_phrases":1685,"slug":1688,"view_count":424,"doi":1689,"paper":1690,"created_at":1705},5244,"Diurnal and Seasonal Surface Urban Heat Island Dynamics and Thermal Field Variance in Faridabad District, India: A MODIS-Based Assessment (2001–2025)","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fajgr\u002F2026\u002Fv9i4489","Aims: To examine the spatial and temporal dynamics of land surface temperature (LST), urban–rural biophysical indices (BUI and NDVI), daily Surface Urban Heat Island Intensity (SUHII), and the Urban Thermal Field Variance Index (UTFVI) on a diurnal and seasonal basis in Faridabad District of northern India. Study Design: Multi-temporal geospatial remote sensing and statistical modelling study. Place and Duration of Study: Faridabad District (Urban Core and Rural Hinterland), Haryana, India; Summer and Winter Seasons Analysed through Six Benchmark Periods (2001, 2005, 2010, 2015, 2020, and 2025). Methodology: MODIS optical (MOD09A1) and thermal infrared (MOD11A2) composite data were analysed using Google Earth Engine at the daytime (10:30 am) and night-time (10:30 pm) overpasses. LST values were grouped into UTFVI (Urban Thermal Field Variance Index) classes. Multivariable OLS regressions and empirical biophysical sensitivities such as Vegetation Cooling Efficiency (VCE) and Impervious Heating Effect (IHE) were evaluated in Python using a stratified random grid (N = 2,500 sampling locations; 235 n 872 pixels). Results: Over the 25-year study period, the built-up gradient for winter (BUI) increased from +0.096 to +0.233 in association with agricultural changes and urban development in the region, while the vegetative deficit for winter (NDVI) increased from -0.061 to -0.136. In terms of temperature, Faridabad showed a SUHI regime during summer (ranging from to ), where the urban core nocturnal LST increased from in 2001 to in 2025, together with regional warming and compression of the urban DTR. The pre-monsoon diurnal regimes showed the SUCI phenomenon (as low as in 2005) owing to the effects of sensible heating in the desiccated fallow rural lands after harvest season. Urban Core thermal field variance showed that moderate thermal stress (UTFVI Class 3) expanded from to , while severe stress tiers (Classes 4–6) concentrated within industrial estates. Daytime vegetation provided consistent cooling (VCE up to per ), whereas nocturnal VCE exhibited temporal variability and diminished transpirational cooling following stomatal closure. Conclusion: Urban greening offers a strong mechanism for daytime heat mitigation but is insufficient to address independently nocturnal heat trapping resulting from the high thermal inertia of construction materials. The sustainable heat-mitigation strategy in industrial nodes entails a combination of urban greening and reflective cool coatings on industrial sheds and impervious surfaces.","目的：考察印度北部法里达巴德县地表温度（LST）、城乡生物物理指数（BUI和NDVI）、日间地表城市热岛强度（SUHII）以及城市热场方差指数（UTFVI）在昼夜和季节尺度上的时空动态。研究设计：多时相地理空间遥感与统计建模研究。研究地点与时长：印度哈里亚纳邦法里达巴德县（城市核心区与乡村腹地）；通过六个基准时段（2001、2005、2010、2015、2020和2025年）分析夏季与冬季。方法：利用Google Earth Engine分析MODIS光学（MOD09A1）和热红外（MOD11A2）合成数据，分别对应白天（上午10：30）和夜间（晚上10：30）过境时刻。LST值按UTFVI（城市热场方差指数）类别分组。在Python中采用分层随机网格（N=2，500个采样点；235 n 872像素）评估多变量OLS回归及经验生物物理敏感性指标，如植被冷却效率（VCE）和不透水面增温效应（IHE）。结果：在25年研究期内，冬季建成区梯度（BUI）从+0.096增至+0.233，与该地区农业变化和城市发展相关；冬季植被亏缺（NDVI）从-0.061增至-0.136。温度方面，法里达巴德在夏季呈现SUHI态势（范围为至），城市核心区夜间LST从2001年的增至2025年的，同时伴随区域增暖和城市日较差（DTR）压缩。季风前昼夜态势因收获季后干燥休耕乡村土地的感热加热效应而呈现SUCI现象（2005年低至）。城市核心区热场方差显示，中度热胁迫（UTFVI第3类）从扩大至，而严重胁迫层级（第4—6类）集中于工业园区。白天植被提供持续冷却（VCE最高达每），而夜间VCE呈现时间变异性，且气孔关闭后蒸腾冷却减弱。结论：城市绿化是白天热缓解的有力机制，但不足以独立应对建筑材料高热惯性导致的夜间热量滞留。工业节点的可持续热缓解策略需要将城市绿化与反射性","Asian Journal of Geographical Research",{"impact":424,"substance":424,"depth":424,"authority":424,"freshness":424,"relevant":424,"comment":1669},"印度城市热岛遥感研究，属城市气候与环境领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[1671],{"name":1667,"url":1664},[1293,1673,1674,1675],"地表温度","城市热岛","MODIS",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1679},{"candidates":1680,"agricultural_evidence":604},[1681],{"code":32,"score":173,"supported":604,"title":1682,"abstract":1683,"tags":1684},[],[711,712],[1673,1293],[1686,1687],"Faridabad 城市热岛","MODIS 地表温度 遥感","Faridabad城市热岛-5244","10.9734\u002Fajgr\u002F2026\u002Fv9i4489",{"doi":1689,"openalex_id":1691,"authors":1692,"venue":1667,"cited_by_count":424,"oa_url":1664,"card":1700,"direction":766,"ingested_from":682},"W7222794736",[1693,1696,1698],{"name":1694,"orcid":1695},"Abhishek Malik","https:\u002F\u002Forcid.org\u002F0009-0009-4878-0871",{"name":1697,"orcid":11},"Shubham Mor",{"name":1699,"orcid":11},"Gaurav Kalotra",{"tldr":1701,"method":1702,"finding":1703,"direction":766,"opportunity":1704},"基于MODIS分析印度Faridabad 2001–2025年昼夜与季节地表城市热岛及热场差异演变。","MODIS光学与热红外数据、Google Earth Engine、OLS回归及","白天绿化降温显著，但夜间建筑热惯性导致的热滞留难以单独缓解。","可结合城乡梯度与作物休耕期感热效应，研究农业用地变化对昼夜热岛的非对称贡献。","2026-10-18T23:30:31.211684Z",{"id":1707,"title":1708,"url":1709,"summary":1710,"summary_zh":1711,"content":11,"source_name":1712,"source_url":1709,"published_at":1618,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1713,"score_detail":1714,"sources":1716,"tags":1718,"primary_topic":1721,"secondary_topics":1722,"classification":1723,"search_phrases":1734,"slug":1737,"view_count":424,"doi":1738,"paper":1739,"created_at":1760},5243,"Identificación de índices de vegetación relacionados con componentes de rendimiento de frijol común (Phaseolus vulgaris L.) mediante el uso de sensores multiespectrales","https:\u002F\u002Fdoi.org\u002F10.18845\u002Ftm.v39i4.8378","Bean production in Costa Rica supplies only 20 % of domestic consumption, requiring the importation of the remaining amount; this situation highlights the need to know in advance the actual productive capacity of local farmers to improve planning, agronomic management, and import regulation. Therefore, the objective of this study was to evaluate the use of multispectral data and vegetation indices to analyze their relationship with yield components in bean varieties cultivated in different production areas of Costa Rica and to identify the yield component with the greatest spectral association. The research was conducted at three sites located in Alajuela, Costa Rica. Multispectral reflectance data were collected during the R5 phenological stage using an airborne multispectral sensor. Subsequently, yield components were quantified per meter of crop row, including 100-grain weight, number of pods per meter, number of plants, number of grains, and total grain weight. The number of pods showed the highest association with spectral variables and was therefore identified as the yield component with the greatest potential for analysis through remote sensing. The SR (Simple Ratio) and GCI (Green Chlorophyll Index) indices showed correlations greater than 0.7 with this variable, being identified as the vegetation indices with the highest spectral association during the R5 stage of the crop.","哥斯达黎加的菜豆（bean）产量仅能满足国内消费量的20%，其余部分需要进口；这一状况凸显了提前了解当地农民实际生产能力的必要性，以改进生产规划、农艺管理和进口调控。因此，本研究旨在评估多光谱数据和植被指数（vegetation indices）的应用，分析其与哥斯达黎加不同产区菜豆品种产量构成因素（yield components）之间的关系，并确定与光谱关联性最强的产量构成因素。研究在哥斯达黎加阿拉胡埃拉（Alajuela）的三个地点开展。在R5物候期使用机载多光谱传感器采集多光谱反射率数据。随后，按每米作物行量化产量构成因素，包括百粒重、每米豆荚数、植株数、籽粒数和籽粒总重。豆荚数与光谱变量的关联性最高，因此被确定为最具遥感分析潜力的产量构成因素。SR（简单比值，Simple Ratio）和GCI（绿色叶绿素指数，Green Chlorophyll Index）与该变量的相关系数大于0.7，被确定为作物R5期光谱关联性最高的植被指数。","Revista Tecnología en Marcha",62,{"impact":173,"substance":361,"depth":715,"authority":416,"freshness":585,"relevant":455,"comment":1715},"该研究利用机载多光谱传感器识别与菜豆产量组分相关的植被指数，方法清晰、结论具体，但属于哥斯达黎加的小区域研究，公共影响有限，可作为遥感作物监测领域的技术性资讯收录。",[1717],{"name":1712,"url":1709},[590,841,711,1719,1720],"植被指数","菜豆",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1724},{"candidates":1725,"agricultural_evidence":604},[1726,1730],{"code":32,"score":416,"supported":604,"title":1727,"abstract":1728,"tags":1729},[],[711,712,606],[841,1719,711],{"code":16,"score":455,"supported":581,"title":1731,"abstract":1732,"tags":1733},[],[],[590],[1735,1736],"哥斯达黎加 菜豆 多光谱 遥感","Phaseolus vulgaris 植被指数 产量","哥斯达黎加菜豆多光谱遥感-5243","10.18845\u002Ftm.v39i4.8378",{"doi":1738,"openalex_id":1740,"authors":1741,"venue":1712,"cited_by_count":424,"oa_url":1754,"card":1755,"direction":766,"ingested_from":682},"W7222720635",[1742,1745,1748,1751],{"name":1743,"orcid":1744},"Guillermo Vargas-Hernández","https:\u002F\u002Forcid.org\u002F0000-0003-1410-3898",{"name":1746,"orcid":1747},"Eddison José Araya-Morales","https:\u002F\u002Forcid.org\u002F0009-0001-0550-414X",{"name":1749,"orcid":1750},"Juan Carlos Hernández","https:\u002F\u002Forcid.org\u002F0000-0002-1915-3213",{"name":1752,"orcid":1753},"Ignacio Mora-Cordero","https:\u002F\u002Forcid.org\u002F0009-0007-0925-0099","https:\u002F\u002Frevistas.tec.ac.cr\u002Findex.php\u002Ftec_marcha\u002Farticle\u002Fdownload\u002F8378\u002F8641",{"tldr":1756,"method":1757,"finding":1758,"direction":766,"opportunity":1759},"用机载多光谱传感器评估菜豆产量构成与植被指数的关系。","机载多光谱遥感获取R5期反射率，结合地面产量构成测定与相关分析。","豆荚数与光谱变量关联最强，SR和GCI指数与其相关系数超过0.7。","可探索将SR\u002FGCI用于菜豆产量早期预测，并扩展到多品种、多生育期与无人机高光谱验证。","2026-10-18T23:30:20.259545Z",{"id":1762,"title":1763,"url":1764,"summary":1765,"summary_zh":1766,"content":11,"source_name":1767,"source_url":1768,"published_at":1769,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":1164,"score_detail":1770,"sources":1772,"tags":1774,"primary_topic":1778,"secondary_topics":1779,"classification":1780,"search_phrases":1803,"slug":1806,"view_count":424,"doi":1807,"paper":1808,"created_at":1844},5183,"Zionist Entity Settlement Expansion and Agricultural Land Conversion in the West Bank, Palestine (2011–2026): A Multi-Temporal Remote Sensing Analysis","https:\u002F\u002Fdoi.org\u002F10.63318\u002Fwaujpasv4i2_60","أُجريت هذه الدراسة لتقييم التغيرات في أنماط الاستيطان التابعة للكيان الصهيوني في الضفة الغربية بفلسطين، وذلك خلال الفترة الممتدة من عام 2011 إلى عام 2026، مع التركيز بشكل خاص على التغيرات التي طرأت على الأراضي الزراعية. وقد وظّف البحث تقنيات الاستشعار عن بعد لتحليل البيانات وتقييم اتجاهات التوسع الاستيطاني في المنطقة المختارة؛ حيث استخدم الباحثون صور الأقمار الصناعية ونظم المعلومات الجغرافية لرصد نمو المستوطنات، وتحديد المناطق المطوّرة حديثاً، ودراسة التغيرات في الغطاء الأرضي. وهدفت الدراسة إلى تحليل المناطق المبنية، والأراضي الزراعية، والغطاء النباتي، والأراضي القاحلة، والموارد المائية، فضلاً عن تحديد بؤر التوسع الاستيطاني، والمقارنة بين التجمعات الاستيطانية المختارة، ورصد التغيرات في شبكة الطرق، ودراسة التحولات في استخدامات الأراضي داخل المنطقة. ولتحقيق أهداف الدراسة، اعتمد الباحثون على مزيج من المنهجيات التي تشمل تصنيف الصور، وكشف التغيرات، وتحليل التراكب، وتحليل البؤر الساخنة. وتمثّل الهدف الرئيسي للدراسة في إرساء أساس لفهم انتشار المستوطنات ورصد التغيرات المرتبطة بالزراعة والتجمعات السكانية المجاورة بشكل أفضل، وذلك بالاستعانة بتقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية.","本研究旨在评估2011年至2026年期间巴勒斯坦约旦河西岸犹太复国主义实体定居模式的变化，特别关注农业用地的变化。研究采用遥感技术分析数据并评估所选地区定居点扩张的趋势；研究人员利用卫星图像和地理信息系统监测定居点的增长、确定新开发区域并研究地表覆盖变化。研究旨在分析建成区、农业用地、植被覆盖、干旱土地和水资源，并确定定居点扩张的热点区域，比较选定的定居点集群，监测道路网络的变化，以及研究区域内土地利用的转变。为实现研究目标，研究人员采用了多种方法相结合的方式，包括图像分类、变化检测、叠加分析和热点分析。研究的主要目标是为理解定居点的扩散以及更好地监测与农业和邻近居民点相关的变化奠定基础，借助遥感技术和地理信息系统实现这一目标。","Wadi Alshatti University Journal of Pure and Applied Sciences","https:\u002F\u002Fwaujpas.com\u002Findex.php\u002Fjournal\u002Farticle\u002Fview\u002F775","2026-10-15T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":173,"relevant":455,"comment":1771},"该研究利用多时相遥感与GIS定量分析2011–2026年西岸定居点扩张导致的农地转换，方法规范、数据跨度长，对农业土地变化监测有参考价值，但地域性强、政策关联度有限，可作国际农业遥感案例收录。",[1773],{"name":1767,"url":1768},[1293,1775,1255,1776,1777],"土地利用变化","农地非农化","巴勒斯坦农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[],{"status":599,"locked":581,"version":4,"method":600,"evidence":1781},{"candidates":1782,"agricultural_evidence":604},[1783,1787,1791,1795,1799],{"code":32,"score":416,"supported":604,"title":1784,"abstract":1785,"tags":1786},[712],[711],[1293],{"code":40,"score":400,"supported":604,"title":1788,"abstract":1789,"tags":1790},[],[1450],[1255],{"code":152,"score":646,"supported":604,"title":1792,"abstract":1793,"tags":1794},[],[992],[],{"code":184,"score":646,"supported":604,"title":1796,"abstract":1797,"tags":1798},[],[1455],[],{"code":176,"score":455,"supported":581,"title":1800,"abstract":1801,"tags":1802},[],[],[1775],[1804,1805],"West Bank 遥感 农地变化","巴勒斯坦 定居点 农地转换","WestBank遥感农地变化-5183","10.63318\u002Fwaujpasv4i2_60",{"doi":1807,"openalex_id":1809,"authors":1810,"venue":1767,"cited_by_count":424,"oa_url":1768,"card":1839,"direction":766,"ingested_from":682},"W7222438368",[1811,1813,1815,1817,1820,1822,1824,1826,1828,1830,1833,1834,1835,1837],{"name":1812,"orcid":11},"أسامة الشلطامي",{"name":1814,"orcid":11},"مصطفى بن حكومة",{"name":1816,"orcid":11},"حمزة الماطوني",{"name":1818,"orcid":1819},"Ramzi SALEM","https:\u002F\u002Forcid.org\u002F0000-0003-1539-1596",{"name":1821,"orcid":11},"Mansour Salem",{"name":1823,"orcid":11},"هالة الخزندار",{"name":1825,"orcid":11},"ياسر نصار",{"name":1827,"orcid":11},"Osama Shaltami",{"name":1829,"orcid":11},"Mustafa Ben Hkoma",{"name":1831,"orcid":1832},"HAMZA AL MATONI","https:\u002F\u002Forcid.org\u002F0000-0002-4748-065X",{"name":1818,"orcid":1819},{"name":1821,"orcid":11},{"name":1836,"orcid":11},"Hala El-Khozondar",{"name":1838,"orcid":11},"Yasser Nassar",{"tldr":1840,"method":1841,"finding":1842,"direction":766,"opportunity":1843},"用多时相遥感分析2011–2026年约旦河西岸定居点扩张及农地转化。","多时相卫星影像与GIS，结合影像分类、变化检测、叠加和热点分析。","定居点扩张显著侵占农地与植被，道路网络扩展驱动土地利用转变。","可延伸至多时相遥感监测土地权属冲突区农地流失，结合高分影像与开放GIS做定量归因。","2026-10-17T23:30:32.831824Z",{"id":1846,"title":1847,"url":1848,"summary":1849,"summary_zh":1850,"content":11,"source_name":1851,"source_url":1848,"published_at":1769,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":45,"score_detail":1852,"sources":1854,"tags":1856,"primary_topic":1860,"secondary_topics":1861,"classification":1864,"search_phrases":1879,"slug":1882,"view_count":424,"doi":1883,"paper":1884,"created_at":1924},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science",{"impact":416,"substance":508,"depth":584,"authority":1094,"freshness":173,"relevant":455,"comment":1853},"方法新颖、结论可靠，但属区域尺度研究，公共影响有限，适合作为技术类精选。",[1855],{"name":1851,"url":1848},[613,711,1857,1858,1859],"土壤碳","Landsat","数字土壤制图",{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},[1862,1863],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":599,"locked":581,"version":4,"method":600,"evidence":1865},{"candidates":1866,"agricultural_evidence":604},[1867,1871,1875],{"code":144,"score":345,"supported":604,"title":1868,"abstract":1869,"tags":1870},[145],[1129,145],[],{"code":24,"score":585,"supported":604,"title":1872,"abstract":1873,"tags":1874},[615],[],[613],{"code":32,"score":173,"supported":604,"title":1876,"abstract":1877,"tags":1878},[],[711,712],[1858,711],[1880,1881],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":1883,"openalex_id":1885,"authors":1886,"venue":1851,"cited_by_count":424,"oa_url":11,"card":1919,"direction":766,"ingested_from":682},"W7222107492",[1887,1890,1893,1896,1899,1902,1905,1908,1910,1913,1916],{"name":1888,"orcid":1889},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":1891,"orcid":1892},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":1894,"orcid":1895},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":1897,"orcid":1898},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":1900,"orcid":1901},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":1903,"orcid":1904},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":1906,"orcid":1907},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":1909,"orcid":11},"A. E. Shatunov",{"name":1911,"orcid":1912},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":1914,"orcid":1915},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":1917,"orcid":1918},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":1920,"method":1921,"finding":1922,"direction":766,"opportunity":1923},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"technologies":1926,"keywords":1963},[1927,1928,1930,1932,1933,1935,1936,1937,1938,1939,1941,1943,1945,1946,1947,1949,1950,1952,1954,1955,1957,1959,1961],{"name":711,"count":157},{"name":1293,"count":1929},109,{"name":591,"count":1931},86,{"name":697,"count":197},{"name":613,"count":1934},24,{"name":1255,"count":1621},{"name":1044,"count":1094},{"name":910,"count":1094},{"name":612,"count":173},{"name":1940,"count":621},"无人机遥感",{"name":1942,"count":408},"高光谱",{"name":1944,"count":408},"高光谱遥感",{"name":593,"count":400},{"name":1559,"count":646},{"name":1948,"count":69},"农业大模型",{"name":977,"count":69},{"name":1951,"count":69},"多源遥感",{"name":1953,"count":69},"数字孪生",{"name":1045,"count":69},{"name":1956,"count":455},"农业机器人",{"name":1958,"count":455},"农产品溯源",{"name":1960,"count":455},"目标检测",{"name":1962,"count":455},"边缘计算",[1964,1966,1968,1970,1971,1972,1973,1974,1976,1978,1980,1981],{"name":590,"count":1965},160,{"name":1235,"count":1967},31,{"name":1969,"count":345},"NDVI",{"name":841,"count":345},{"name":210,"count":1094},{"name":1099,"count":524},{"name":1719,"count":385},{"name":1975,"count":385},"水资源管理",{"name":1977,"count":585},"Sentinel-2",{"name":1979,"count":585},"作物分类",{"name":218,"count":585},{"name":797,"count":585}]