[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5238":3,"related-5238":67},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":8,"secondary_topics":30,"classification":31,"search_phrases":46,"slug":49,"view_count":20,"doi":50,"paper":51,"created_at":66},5238,"Integrating Time Series, Remote Sensing and Computational Intelligence for Flood Prediction in a Data-Scarce Tropical Floodplain of the Lower Magdalena River Basin, Colombia","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10374442\u002Fv1","Integrating Time Series, Remote Sensing and Computational Intelligence for Flood Prediction in a Data-Scarce Tropical Floodplain of the Lower Magdalena River Basin, Colombia。Research Square",null,"Research Square","2026-10-16T00:00:00Z","论文",10,false,56,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":16,"relevant":20,"comment":21},8,18,16,6,1,"该研究将遥感与计算智能结合用于数据稀缺的热带洪泛区洪水预测，方法有一定新意，但属于区域性研究，且来自预印本平台，权威性有限，不宜进入每日精选。",[23],{"name":9,"url":6},[25,26,27,28,29],"遥感","时间序列","洪水预测","计算智能","哥伦比亚",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":35},"needs_review","2026-10-22.2","rules",{"candidates":36,"agricultural_evidence":13,"reason":45},[37],{"code":38,"score":39,"supported":40,"title":41,"abstract":43,"tags":44},"I3",12,true,[42],"remote sensing",[42],[25],"农业相关性需要复核",[47,48],"哥伦比亚 时间序列 洪水预测 计算智能","哥伦比亚 时间序列","哥伦比亚时间序列洪水预测计算智能-5238","10.21203\u002Frs.3.rs-10374442\u002Fv1",{"doi":50,"openalex_id":52,"authors":53,"venue":9,"cited_by_count":62,"oa_url":63,"card":8,"direction":64,"ingested_from":65},"W7222467797",[54,57,59],{"name":55,"orcid":56},"Jaime Alberto Parra Plazas","https:\u002F\u002Forcid.org\u002F0000-0002-4992-5077",{"name":58,"orcid":8},"Juan Sebastian Parra-Perdomo",{"name":60,"orcid":61},"Leonardo Plazas-Nossa","https:\u002F\u002Forcid.org\u002F0000-0002-2223-9886",0,"https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-10374442\u002Flatest.pdf","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:09.385433Z",{"total":19,"page":20,"page_size":19,"items":68},[69,158,225,278,322,397],{"id":70,"title":71,"url":72,"summary":73,"summary_zh":74,"content":8,"source_name":75,"source_url":72,"published_at":76,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":77,"score_detail":78,"sources":83,"tags":85,"primary_topic":89,"secondary_topics":94,"classification":95,"search_phrases":131,"slug":134,"view_count":126,"doi":135,"paper":136,"created_at":157},3155,"Time Series Extrinsic Regression for Smart Agriculture: Field‐Level Crop Yield Prediction From Multisource Agricultural Time Series","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fqre.70406","ABSTRACT The growing availability of temporal data in agriculture has created new opportunities for data‐driven decision support systems aimed at improving the efficiency, sustainability, and adaptability of agricultural systems. Remote sensing platforms, weather‐related datasets, and field‐management records provide information on crop development and environmental conditions throughout the growing season. In this context, predictive tasks such as crop yield prediction, irrigation requirement assessment, and nutrient demand estimation can be naturally formulated as Time Series Extrinsic Regression (TSER), a supervised learning task in which a continuous target is predicted from one or more time series. This paper presents TSER within the Smart Agriculture domain and investigates its applicability to field‐level crop yield prediction using multisource agricultural time series. The study adopts a field‐based temporal approach where Sentinel‐2 observations are processed to derive vegetation‐index time series, spatially aggregated at parcel level, and integrated with ERA5‐Land weather‐related variables and static vineyard attributes. Representative neural and non‐neural TSER methods are compared under temporal and spatial generalization scenarios. Results show that TSER provides a suitable supervised learning framework for crop yield prediction from heterogeneous agricultural time series. Conditional LSTM achieves the best overall performance under temporal generalization across vintages, while non‐neural methods remain competitive under spatial generalization to unseen vineyard blocks. These findings suggest that TSER can support field‐level predictive modeling in Smart Agriculture, with model choice depending on the target generalization setting, data availability, and operational constraints.","摘要 农业中时间数据的日益丰富为数据驱动的决策支持系统创造了新的机遇，旨在提高农业系统的效率、可持续性和适应性。遥感平台、气象相关数据集和田间管理记录提供了整个生长季作物发育和环境状况的信息。在此背景下，作物产量预测、灌溉需求评估和养分需求估算等预测任务可以自然地表述为时间序列外在回归（TSER），这是一种监督学习任务，即从一个或多个时间序列中预测连续目标变量。本文在智慧农业领域内介绍TSER，并研究其利用多源农业时间序列进行田块级作物产量预测的适用性。本研究采用基于田块的时间方法，对Sentinel-2观测数据进行处理以导出植被指数时间序列，在地块尺度上进行空间聚合，并与ERA5-Land气象相关变量及静态葡萄园属性相整合。在时间泛化和空间泛化情景下，比较了具有代表性的神经与非神经TSER方法。结果表明，TSER为从异质农业时间序列进行作物产量预测提供了一个合适的监督学习框架。条件LSTM在跨年份的时间泛化下取得了最佳整体性能，而非神经方法在向未见葡萄园地块的空间泛化下仍具有竞争力。这些发现表明，TSER能够支持智慧农业中的田块级预测建模，模型选择取决于目标泛化设置、数据可用性和操作约束。","Quality and Reliability Engineering International","2026-09-22T00:00:00Z",72,{"impact":39,"substance":79,"depth":80,"authority":81,"freshness":12,"relevant":20,"comment":82},20,17,13,"将时间序列外在回归引入田块级产量预测，方法新颖、结论有对比依据，但属学术论文，产业影响有限。",[84],{"name":75,"url":72},[86,87,25,88,26],"智慧农业","农业人工智能","作物产量预测",{"code":38,"slug":90,"name":91,"path":92,"parent_code":93},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":96,"locked":13,"version":33,"method":34,"evidence":97},"classified",{"candidates":98,"agricultural_evidence":40},[99,106,110,117,124],{"code":100,"score":101,"supported":40,"title":102,"abstract":104,"tags":105},"I1",15,[103],"smart agriculture",[86,103],[86],{"code":38,"score":16,"supported":40,"title":107,"abstract":108,"tags":109},[],[25,42],[88,25],{"code":111,"score":19,"supported":40,"title":112,"abstract":113,"tags":116},"H3",[],[114,115],"灌溉","irrigation",[],{"code":118,"score":119,"supported":40,"title":120,"abstract":121,"tags":123},"I2",4,[],[122],"decision support",[87],{"code":125,"score":126,"supported":40,"title":127,"abstract":128,"tags":130},"B2",3,[],[129],"葡萄",[],[132,133],"Sentinel-2 作物产量预测","TSER 智慧农业","Sentinel-2作物产量预测-3155","10.1002\u002Fqre.70406",{"doi":135,"openalex_id":137,"authors":138,"venue":75,"cited_by_count":62,"oa_url":72,"card":151,"direction":64,"ingested_from":65},"W7213984793",[139,142,145,147,149],{"name":140,"orcid":141},"Amalia Vanacore","https:\u002F\u002Forcid.org\u002F0000-0002-0320-5541",{"name":143,"orcid":144},"Armando Ciardiello","https:\u002F\u002Forcid.org\u002F0009-0005-7272-9663",{"name":146,"orcid":8},"Gennaro Pio Auricchio",{"name":148,"orcid":8},"Luigi Uccello",{"name":150,"orcid":8},"Annalisa Izzo",{"tldr":152,"method":153,"finding":154,"direction":155,"opportunity":156},"将田块级作物产量预测建模为时间序列外回归，比较多源农业时间序列上的神经与非神经方法。","Sentinel-2植被指数、ERA5-Land气象与葡萄园静态属性，对比LST","条件LSTM在跨年份时间泛化最优，非神经方法在空间泛化到新地块时仍有竞争力。","农业遥感与作物表型","可探索多作物、多区域下TSER模型迁移性与不确定性量化，及与农事决策系统耦合。","2026-09-22T23:30:10.381662Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":163,"content":8,"source_name":164,"source_url":161,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":166,"score_detail":167,"sources":170,"tags":172,"primary_topic":175,"secondary_topics":179,"classification":180,"search_phrases":208,"slug":211,"view_count":62,"doi":212,"paper":213,"created_at":224},5588,"Certified Temporal Fusion of Artificial Intelligence Climate Analytics for Crop Failure Early Warning Under Sparse Agricultural Sensors: Error Bounds and Famine Relief Targeting","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545489","Sparse agricultural sensor networks across sub-Saharan Africa leave crop-failure early-warning systems with too little ground observation to support reliable famine relief targeting. The framework treats sensor sparsity as a missing-data problem with a known observation operator and propagates climate-analytics uncertainty through a temporal Kalman-Buys filter whose gain is constrained by a certified risk envelope. Using NASA POWER monthly reanalysis for Accra, Ghana (2000-2023, 312 observations), the analysis establishes a mean temperature of 26.521 C and a warming trend of 0.1622 C per decade, which calibrates the climate-analytics prior. The derived bounds show that relief-targeting error scales with the square root of sensor density and with the Lipschitz constant of the crop-response function, yielding an explicit sensor-placement rule for a target misclassification rate. The contribution is a certified, assumption-explicit bridge between climate analytics and famine relief targeting that states the conditions under which early warning is trustworthy.","撒哈拉以南非洲稀疏的农业传感器网络使得作物歉收预警系统缺乏足够的地面观测数据，难以支撑可靠的饥荒救援目标定位。该框架将传感器稀疏性视为具有已知观测算子的缺失数据问题，并通过时间Kalman-Buys滤波器传播气候分析不确定性，该滤波器的增益受经认证的风险包络约束。利用NASA POWER对加纳阿克拉的月度再分析数据（2000—2023年，312个观测值），分析确定了平均气温为26.521°C、每十年0.1622°C的增暖趋势，据此校准了气候分析先验。推导出的界限表明，救援目标定位误差随传感器密度的平方根以及作物响应函数的Lipschitz常数缩放，从而为目标误分类率给出了明确的传感器布设规则。该研究的贡献在于构建了一个经认证的、假设明确的气候分析与饥荒救援目标定位之间的桥梁，阐明了早期预警可信赖的条件。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",74,{"impact":39,"substance":168,"depth":17,"authority":39,"freshness":12,"relevant":20,"comment":169},22,"该研究提出一种带误差界认证的时序融合方法，用于稀疏传感器条件下的作物歉收预警与饥荒救援瞄准，方法新颖、结论明确，但案例局限于加纳阿克拉，产业影响有限，适合作为技术前沿条目收录。",[171],{"name":164,"url":161},[86,87,173,25,174],"粮食安全","气候分析",{"code":118,"slug":176,"name":177,"path":178,"parent_code":93},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":96,"locked":13,"version":33,"method":34,"evidence":181},{"candidates":182,"agricultural_evidence":40},[183,189,195,200,204],{"code":118,"score":184,"supported":40,"title":185,"abstract":187,"tags":188},9,[186],"artificial intelligence",[],[87],{"code":190,"score":126,"supported":40,"title":191,"abstract":192,"tags":194},"I5",[],[193],"农业传感",[],{"code":196,"score":20,"supported":13,"title":197,"abstract":198,"tags":199},"A8",[],[],[173],{"code":100,"score":20,"supported":13,"title":201,"abstract":202,"tags":203},[],[],[86],{"code":38,"score":20,"supported":13,"title":205,"abstract":206,"tags":207},[],[],[25],[209,210],"NASA POWER 加纳 气候分析","作物歉收 早期预警 传感器","NASAPOWER加纳气候分析-5588","10.5281\u002Fzenodo.23545489",{"doi":212,"openalex_id":214,"authors":215,"venue":164,"cited_by_count":62,"oa_url":161,"card":218,"direction":223,"ingested_from":65},"W7223111766",[216],{"name":217,"orcid":8},"Fatima-Zahra El Khattabi",{"tldr":219,"method":220,"finding":221,"direction":155,"opportunity":222},"提出带误差界的时间融合框架，在稀疏传感器下用气候分析预警作物歉收。","卡尔曼-布西滤波+NASA POWER再分析数据，风险包络约束增益。","救济瞄准误差随传感器密度平方根和作物响应Lipschitz常数缩放，给出布点规则。","可将该认证误差界框架扩展到多作物多区域，结合卫星遥感弥补地面传感器稀疏。","农业人工智能与决策模型","2026-10-22T11:21:02.682497Z",{"id":226,"title":227,"url":228,"summary":229,"summary_zh":8,"content":8,"source_name":9,"source_url":228,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":230,"score_detail":231,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":243,"classification":244,"search_phrases":256,"slug":259,"view_count":62,"doi":260,"paper":261,"created_at":277},5579,"A Composite Field-Evaluation Framework for Digital Agriculture Maps","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11283264\u002Fv1","A Composite Field-Evaluation Framework for Digital Agriculture Maps。Research Square",38,{"impact":16,"substance":19,"depth":16,"authority":19,"freshness":12,"relevant":20,"comment":232},"预印本提出数字农业地图的田间复合评估框架，方法有一定新意，但摘要信息极少、未经同行评审，公共影响有限，暂不宜进入每日精选。",[234],{"name":9,"url":228},[236,237,25,238],"数字农业","农业信息化","地图评估",{"code":100,"slug":240,"name":241,"path":242,"parent_code":93},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":96,"locked":13,"version":33,"method":34,"evidence":245},{"candidates":246,"agricultural_evidence":40},[247,252],{"code":100,"score":81,"supported":40,"title":248,"abstract":250,"tags":251},[249],"digital agriculture",[249],[237,236],{"code":38,"score":20,"supported":13,"title":253,"abstract":254,"tags":255},[],[],[25],[257,258],"Research Square 数字农业","农业信息化 地图评估 数字农业 遥感","ResearchSquare数字农业-5579","10.21203\u002Frs.3.rs-11283264\u002Fv1",{"doi":260,"openalex_id":262,"authors":263,"venue":9,"cited_by_count":62,"oa_url":228,"card":8,"direction":276,"ingested_from":65},"W7223099987",[264,267,270,273],{"name":265,"orcid":266},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":268,"orcid":269},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":271,"orcid":272},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":274,"orcid":275},"Louis Longchamps","https:\u002F\u002Forcid.org\u002F0000-0002-4761-6094","数字乡村与农业信息化","2026-10-22T11:20:47.463814Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":8,"source_name":284,"source_url":281,"published_at":285,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":62,"score_detail":286,"sources":288,"tags":290,"primary_topic":8,"secondary_topics":293,"classification":294,"search_phrases":301,"slug":304,"view_count":62,"doi":305,"paper":306,"created_at":321},5574,"Aggregation of results of automatic classification of remote sensing data of natural objects","https:\u002F\u002Fdoi.org\u002F10.31772\u002F2712-8970-2026-27-3-390-399","Methods for aggregating the results of automatic classification of multidimensional spectral remote sensing data are proposed. This task is accomplished using a modified «Forel» algorithm and a procedure for testing the hypothesis of the homogeneity of the distribution laws of random variables characterizing the detected classes. When automatic classification algorithms are designed to detect sets of random variable values corresponding to unimodal fragments of their probability density, it is reasonable to regroup classes within the space of their mean random variable values. This is implemented using a modification of the “Forel” algorithm, the parameters of which are determined based on the results of a computational experiment. If additional information about the properties of a natural object is available, it can be used to aggregate the results of automatic classification in the spectral feature space. Under these conditions, class aggregation is based on testing the hypothesis of the homogeneity of the distribution laws of the random variables that characterize the additional information. To solve this problem, a nonparametric pattern recognition algorithm that follows the maximum likelihood criterion is used. The optimal blur coefficients of the classification algorithm's kernel functions are selected based on the requirement to minimize the pattern recognition error estimate in the “sliding exam” mode. The obtained results are summarized in the construction of an information graph, the nodes of which correspond to classes. An edge is formed between two graph nodes if the pattern recognition error for a given pair of classes does not differ significantly from 0,5. A modification of the Kolmogorov–Smirnov test is used to test the hypotheses. A macroclass is formed by classes corresponding to a complete subgraph that do not reliably differ in the distribution patterns of features in the additional information. The sequential procedure for forming macroclasses begins with finding the complete subgraph with the maximum number of classes. The results obtained form the basis for developing a decision rule for assessing the membership of spectral feature values in the resulting macroclasses. The proposed method for aggregating the results of automatic classification is relevant for the system analysis of remote sensing data.","针对多维光谱遥感数据自动分类结果的聚合方法被提出。该任务通过改进的“Forel”算法以及用于检验表征所检测类别的随机变量分布律同质性假设的程序来完成。当自动分类算法旨在检测对应于其概率密度单峰片段的随机变量值集合时，在其均值随机变量值空间内重新分组类别是合理的。这通过“Forel”算法的改进版本实现，其参数基于计算实验结果确定。如果存在关于自然对象属性的附加信息，则可将其用于聚合光谱特征空间中的自动分类结果。在这些条件下，类别聚合基于检验表征附加信息的随机变量分布律同质性假设。为解决该问题，采用遵循最大似然准则的非参数模式识别算法。分类算法核函数的最优模糊系数基于在“滑动检验”模式下最小化模式识别误差估计的要求进行选择。所得结果汇总于信息图的构建中，其节点对应于类别。如果给定类别对的模式识别误差与0.5无显著差异，则在两个图节点之间形成边。使用Kolmogorov–Smirnov检验的改进版本来检验假设。宏类由对应于完全子图的类别形成，这些类别在附加信息中的特征分布模式上没有可靠差异。形成宏类的顺序过程从寻找具有最大类别数的完全子图开始。所得结果构成了开发决策规则的基础，该规则用于评估光谱特征值在所得宏类中的隶属关系。所提出的用于聚合自动分类结果的方法对于遥感数据的系统分析具有相关性。","Siberian Aerospace Journal","2026-10-20T00:00:00Z",{"impact":62,"substance":62,"depth":62,"authority":62,"freshness":62,"relevant":62,"comment":287},"该文为遥感数据自动分类结果聚合的算法论文，属通用遥感方法研究，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。",[289],{"name":284,"url":281},[25,291,292],"光谱数据","自动分类",[],{"status":32,"locked":13,"version":33,"method":34,"evidence":295},{"candidates":296,"agricultural_evidence":13,"reason":45},[297],{"code":38,"score":101,"supported":40,"title":298,"abstract":299,"tags":300},[42],[25,42],[25],[302,303],"光谱数据 自动分类 遥感","光谱数据 自动分类","光谱数据自动分类遥感-5574","10.31772\u002F2712-8970-2026-27-3-390-399",{"doi":305,"openalex_id":307,"authors":308,"venue":284,"cited_by_count":62,"oa_url":315,"card":316,"direction":155,"ingested_from":65},"W7223058937",[309,312],{"name":310,"orcid":311},"Aleksandr V. Lapko","https:\u002F\u002Forcid.org\u002F0000-0002-0664-3870",{"name":313,"orcid":314},"Vasiliy A. Lapko","https:\u002F\u002Forcid.org\u002F0000-0001-6938-9323","https:\u002F\u002Fjournals.eco-vector.com\u002F2712-8970\u002Farticle\u002Fdownload\u002F719886\u002F228882",{"tldr":317,"method":318,"finding":319,"direction":155,"opportunity":320},"提出聚合多维光谱遥感自动分类结果的方法，合并相似类别为宏类。","改进Forel算法、非参数模式识别、Kolmogorov-Smirnov检验与信","基于分布同质性检验可有效聚合自动分类类别，形成稳定宏类。","可将该聚合框架用于作物遥感分类后处理，提升类别一致性与可解释性。","2026-10-22T11:20:42.307504Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":8,"source_name":328,"source_url":325,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":329,"score_detail":330,"sources":333,"tags":335,"primary_topic":340,"secondary_topics":345,"classification":346,"search_phrases":377,"slug":380,"view_count":62,"doi":381,"paper":382,"created_at":396},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies",61,{"impact":16,"substance":17,"depth":331,"authority":39,"freshness":184,"relevant":20,"comment":332},14,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[334],{"name":328,"url":325},[25,336,337,338,339],"土地利用","土壤侵蚀","GIS","水土保持",{"code":341,"slug":342,"name":343,"path":344,"parent_code":93},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":96,"locked":13,"version":33,"method":34,"evidence":347},{"candidates":348,"agricultural_evidence":40},[349,355,360,367,371],{"code":341,"score":12,"supported":40,"title":350,"abstract":351,"tags":354},[],[352,338,353],"地理信息","geographic information",[338],{"code":38,"score":356,"supported":40,"title":357,"abstract":358,"tags":359},7,[],[25,42],[25],{"code":361,"score":19,"supported":40,"title":362,"abstract":363,"tags":366},"H2",[],[364,365],"土壤","soil",[],{"code":111,"score":126,"supported":40,"title":368,"abstract":369,"tags":370},[],[114],[],{"code":372,"score":373,"supported":13,"title":374,"abstract":375,"tags":376},"H6",2,[],[],[336,339],[378,379],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":381,"openalex_id":383,"authors":384,"venue":328,"cited_by_count":62,"oa_url":8,"card":390,"direction":155,"ingested_from":65},"W7223117733",[385,387],{"name":386,"orcid":8},"MERİÇ KURT",{"name":388,"orcid":389},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":391,"method":392,"finding":393,"direction":394,"opportunity":395},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":398,"title":399,"url":400,"summary":401,"summary_zh":402,"content":8,"source_name":403,"source_url":400,"published_at":165,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":404,"score_detail":405,"sources":407,"tags":409,"primary_topic":412,"secondary_topics":413,"classification":414,"search_phrases":431,"slug":434,"view_count":62,"doi":435,"paper":436,"created_at":455},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":39,"substance":79,"depth":80,"authority":331,"freshness":12,"relevant":20,"comment":406},"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。",[408],{"name":403,"url":400},[86,410,25,411],"作物制图","SAR",{"code":38,"slug":90,"name":91,"path":92,"parent_code":93},[],{"status":96,"locked":13,"version":33,"method":34,"evidence":415},{"candidates":416,"agricultural_evidence":40},[417,422,427],{"code":38,"score":12,"supported":40,"title":418,"abstract":419,"tags":421},[],[25,42,420],"multispectral",[25],{"code":196,"score":19,"supported":40,"title":423,"abstract":424,"tags":426},[],[173,425],"food security",[],{"code":100,"score":20,"supported":13,"title":428,"abstract":429,"tags":430},[],[],[86],[432,433],"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":435,"openalex_id":437,"authors":438,"venue":403,"cited_by_count":62,"oa_url":400,"card":450,"direction":155,"ingested_from":65},"W7223117203",[439,442,444,447],{"name":440,"orcid":441},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":443,"orcid":8},"Wentao Lv",{"name":445,"orcid":446},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":448,"orcid":449},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":451,"method":452,"finding":453,"direction":155,"opportunity":454},"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"]