[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4684":3,"related-4684":84},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":52,"slug":55,"view_count":56,"doi":57,"paper":58,"created_at":83},4684,"Índices espectrales y salud ecosistémica en el monitoreo de manglares: una revisión sistemática de los enfoques de teledetección óptica","https:\u002F\u002Fdoi.org\u002F10.26359\u002F52462.0804","Optical remote sensing has become a key tool for monitoring mangrove ecosystems and assessing their spatial and temporal dynamics. However, many studies evaluate spectral indices primarily from an applied perspective, often overlooking the methodological evolution and ecological questions that originally motivated their development. This study presents an interpretive systematic review structured following the general PRISMA 2020 framework (n = 40 articles) to reconstruct the historical and methodological trajectory of spectral metrics used to characterize mangrove ecosystem health. The results reveal a recent predominance of Sentinel-2 imagery (55% of the reviewed studies) and a persistent reliance on the Normalized Difference Vegetation Index (NDVI), which appears in approximately 85% of the literature analyzed. The bibliographic synthesis identified four dominant methodological transitions, ranging from early land-cover detection approaches to automated time-series analyses supported by cloud-computing platforms, particularly Google Earth Engine (GEE). The reviewed evidence suggests that indices based exclusively on canopy greenness have limitations for interpreting degradation processes in intertidal environments, where hydrological changes may precede detectable shifts in vegetation cover. Multi-index strategies that combine moisture-sensitive metrics (e.g., NDMI or NDWI) with vegetation-vigor or background-corrected metrics (e.g., EVI or SAVI) may provide a more informative framework. Their performance remains context dependent and should be interpreted in relation to tidal regime, canopy structure, spatial and temporal resolution, and ecological field validation. Finally, the study identifies key methodological gaps related to empirical validation, uncertainty quantification, and the interpretation of spectral signals in complex coastal environments. Addressing these limitations may improve the reliability of remote-sensing-based monitoring. The application to Estero Pargo, Jaina, and Atasta in the Campeche State, Mexico, is presented as a methodological translation of patterns identified in the reviewed literature rather than as direct empirical validation of spectral-index performance at those sites. Keywords: Remote Sensing, mangroves, spectral indices, sentinel-2, ecosystem health, coastal ecosystems.","光学遥感已成为监测红树林生态系统及其时空动态的关键工具。然而，许多研究主要从应用角度评价光谱指数，往往忽视了最初推动其发展的方法论演进和生态学问题。本研究按照PRISMA 2020通用框架（n = 40篇文献）开展了解释性系统综述，以重建用于表征红树林生态系统健康的光谱指标的历史与方法论轨迹。结果表明，近年来Sentinel-2影像占据主导地位（占所综述研究的55%），而归一化差异植被指数（NDVI）则持续被依赖，在约85%的分析文献中出现。文献综合识别出四个主要的方法论转变，从早期的土地覆盖探测方法到由云计算平台（尤其是Google Earth Engine，GEE）支持的自动化时间序列分析。所综述的证据表明，仅基于冠层绿度的指数在解释潮间带环境退化过程方面存在局限，因为水文变化可能先于可检测的植被覆盖变化。将水分敏感指标（如NDMI或NDWI）与植被活力或背景校正指标（如EVI或SAVI）相结合的多指数策略可能提供更具信息量的框架。其表现仍依赖于具体情境，应结合潮汐状况、冠层结构、时空分辨率和生态野外验证加以解读。最后，本研究识别了与经验验证、不确定性量化以及复杂海岸环境中光谱信号解读相关的关键方法论空白。解决这些局限可能提高基于遥感的监测可靠性。对墨西哥坎佩切州Estero Pargo、Jaina和Atasta的应用是作为对所综述文献中所识别模式的方法论转化而提出的，而非对这些地点光谱指数表现的直接经验验证。关键词：遥感，红树林，光谱指数，Sentinel-2，生态系统健康，海岸生态系统。",null,"JAINA Costas y Mares ante el Cambio Climático","2026-10-09T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},8,20,17,9,1,"系统综述梳理红树林光学遥感光谱指数的方法演进与局限，提出多指数组合框架，对海岸带生态监测有参考价值，但属区域性学术综述，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","红树林","生态系统健康","光谱指数","海岸带监测",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":51},[38,46],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":45},"I3",7,true,[],[26,44],"remote sensing",[26],{"code":47,"score":21,"supported":14,"title":48,"abstract":49,"tags":50},"H6",[],[],[27],"农业相关性需要复核",[53,54],"Sentinel-2 红树林 遥感监测","墨西哥 Campeche 红树林 光谱指数","Sentinel-2红树林遥感监测-4684",2,"10.26359\u002F52462.0804",{"doi":57,"openalex_id":59,"authors":60,"venue":10,"cited_by_count":74,"oa_url":75,"card":76,"direction":80,"ingested_from":82},"W7221141211",[61,63,66,69,71],{"name":62,"orcid":9},"Hector Guadalupe Manzanilla",{"name":64,"orcid":65},"Carlos Omar Cruz Gaistardo","https:\u002F\u002Forcid.org\u002F0009-0008-5027-3548",{"name":67,"orcid":68},"Mónica Cristina Rodríguez-Palacio","https:\u002F\u002Forcid.org\u002F0000-0001-5643-958X",{"name":70,"orcid":9},"María del Rocío Zarate Hernández",{"name":72,"orcid":73},"Claudia M. Agraz-Hernández","https:\u002F\u002Forcid.org\u002F0000-0001-9717-4514",0,"https:\u002F\u002Fjainaccc.uacam.mx\u002Fjainaccc\u002Farticle\u002Fdownload\u002F73\u002F62",{"tldr":77,"method":78,"finding":79,"direction":80,"opportunity":81},"系统综述红树林光谱指数监测方法，梳理其历史演变并指出验证与不确定性等空白。","PRISMA 2020系统综述40篇文献，分析Sentinel-2、NDVI、G","NDVI主导但仅反映绿度，多指数结合水分与背景校正指标更适于潮间带退化监测。","农业遥感与作物表型","可构建融合潮汐、冠层结构与多光谱指数的红树林健康监测与不确定性量化框架。","openalex","2026-10-11T23:30:31.715184Z",{"total":85,"page":21,"page_size":85,"items":86},6,[87,153,224,279,324,396],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":93,"source_url":90,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":101,"tags":103,"primary_topic":9,"secondary_topics":107,"classification":108,"search_phrases":126,"slug":129,"view_count":74,"doi":130,"paper":131,"created_at":152},4610,"UAV-based estimation of aboveground biomass carbon in the Sundarbans mangrove forest of Bangladesh","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faeb18f","Abstract Reliable biomass and carbon assessments are essential for understanding mangrove forest dynamics and their role in global carbon cycling. However, field-based biomass estimation is time-consuming, labour-intensive, and costly. Remote sensing provides a practical and cost-effective alternative, particularly with unmanned aerial vehicles (UAVs) that can capture high-resolution imagery. The assessment of biomass carbon in the Sundarbans Mangrove Forest (SMF) using high-resolution remote sensing remains understudied. This study develops an integrated carbon assessment framework for estimating aboveground biomass carbon in the SMF using UAV-derived canopy metrics and field measurements. Error propagation and feature importance analyses were performed to quantify uncertainty and assess predictor influence, providing a robust and uncertainty-aware approach for mangrove biomass carbon assessment. CPA was used to estimate proxy DBH using linear, polynomial, logarithmic, power, and exponential regression models. All CPA–DBH models showed comparable performance (R2 = 0.73-0.75). The linear and polynomial models achieved the lowest RMSE (1.77 cm), while the polynomial model produced the lowest MAE (1.22 cm) and was therefore selected for estimating Proxy DBH. Field-based AGBC was calculated from measured tree height and DBH using species-specific and generic allometric equations applicable to the Sundarbans, whereas UAV-based AGBC was estimated using UAV-derived Mean DSM Height and CPA-derived Proxy DBH. Simple linear regression (SLR), multiple linear regression (MLR), and machine-learning (ML) models (Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting) were evaluated for AGBC prediction. The MLR model showed the best performance on the validation\u002Fmodel-selection subset (R² = 0.86, RMSE = 14.70 Mg C ha⁻¹, MedAE = 4.58 Mg C ha⁻¹). UAV-derived AGBC estimates closely matched field measurements and previously reported values for the Sundarbans, demonstrating the potential of high-resolution UAV metrics for reliable mangrove AGBC estimation and supporting carbon monitoring, REDD+ initiatives, and sustainable mangrove management in Bangladesh.","可靠的生物量和碳评估对于理解红树林动态及其在全球碳循环中的作用至关重要。然而，基于实地调查的生物量估算耗时、费力且成本高昂。遥感提供了一种实用且具有成本效益的替代方案，尤其是利用能够获取高分辨率影像的无人机（UAV）。利用高分辨率遥感对孙德尔本斯红树林（SMF）进行生物量碳评估的研究仍较为缺乏。本研究开发了一个综合碳评估框架，利用无人机获取的冠层指标和实地测量数据估算SMF的地上生物量碳。通过误差传播和特征重要性分析量化不确定性并评估预测因子的影响，为红树林生物量碳评估提供了一种稳健且考虑不确定性的方法。采用CPA（冠层投影面积）通过线性、多项式、对数、幂函数和指数回归模型估算代理DBH（胸径）。所有CPA–DBH模型均表现出相近的性能（R² = 0.73–0.75）。线性模型和多项式模型取得了最低的RMSE（1.77 cm），而多项式模型产生了最低的MAE（1.22 cm），因此被选用于估算代理DBH。基于实地的AGBC（地上生物量碳）通过实测树高和DBH，采用适用于孙德尔本斯的物种特异性异速生长方程和通用异速生长方程计算，而基于无人机的AGBC则利用无人机获取的平均DSM高度和CPA衍生的代理DBH进行估算。采用简单线性回归（SLR）、多元线性回归（MLR）和机器学习（ML）模型（随机森林、支持向量机、人工神经网络和极端梯度提升）评估AGBC预测性能。MLR模型在验证\u002F模型选择子集上表现最佳（R² = 0.86，RMSE = 14.70 Mg C ha⁻¹，MedAE = 4.58 Mg C ha⁻¹）。无人机获取的AGBC估算值与实地测量结果及先前报道的孙德尔本斯数值高度吻合，表明高分辨率无人机指标在可靠估算红树林AGBC方面具有潜力，可支持孟加拉国的碳监测、REDD+倡议和可持续红树林管理。","Environmental Research Communications","2026-10-07T00:00:00Z",80,{"impact":97,"substance":98,"depth":97,"authority":99,"freshness":17,"relevant":21,"comment":100},18,22,14,"该研究利用无人机高分辨率影像与机器学习模型估算孟加拉国孙德尔本斯红树林地上生物量碳，方法新颖、数据可靠，对林业碳汇监测与REDD+具有参考价值，但属境外案例、应用范围有限，可作为专业资讯收录。",[102],{"name":93,"url":90},[104,26,105,27,106],"无人机","智慧林业","碳汇计量",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":109},{"candidates":110,"agricultural_evidence":14,"reason":51},[111,115,122],{"code":39,"score":40,"supported":41,"title":112,"abstract":113,"tags":114},[],[26,44],[26],{"code":116,"score":117,"supported":41,"title":118,"abstract":119,"tags":121},"I2",3,[],[120],"机器学习",[],{"code":47,"score":21,"supported":14,"title":123,"abstract":124,"tags":125},[],[],[27],[127,128],"孟加拉国 孙德尔本斯 红树林 无人机","无人机 生物量碳 遥感估算","孟加拉国孙德尔本斯红树林无人机-4610","10.1088\u002F2515-7620\u002Faeb18f",{"doi":130,"openalex_id":132,"authors":133,"venue":93,"cited_by_count":74,"oa_url":145,"card":146,"direction":151,"ingested_from":82},"W7220935167",[134,137,140,142],{"name":135,"orcid":136},"Md Abul Hashem","https:\u002F\u002Forcid.org\u002F0009-0006-2712-3168",{"name":138,"orcid":139},"Md Sarwar Hossain","https:\u002F\u002Forcid.org\u002F0000-0001-6101-659X",{"name":141,"orcid":9},"Md. Tauhidor Rahaman",{"name":143,"orcid":144},"Brian Barrett","https:\u002F\u002Forcid.org\u002F0000-0002-4380-4020","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faeb18f\u002Fpdf",{"tldr":147,"method":148,"finding":149,"direction":80,"opportunity":150},"利用无人机冠层指标与地面数据估算孟加拉孙德尔本斯红树林地上生物量碳。","无人机高分辨率影像提取冠层指标，结合CPA估算DBH，用MLR与机器学习模型预测","多元线性回归表现最佳（R²=0.86），无人机估算值与实测及历史值高度吻合。","可探索多时相无人机与卫星融合，提升红树林碳储量动态监测及不确定性量化能力。","农业绿色发展与碳","2026-10-10T23:31:07.399747Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":158,"content":9,"source_name":159,"source_url":156,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":161,"score_detail":162,"sources":165,"tags":167,"primary_topic":172,"secondary_topics":177,"classification":178,"search_phrases":207,"slug":210,"view_count":74,"doi":211,"paper":212,"created_at":223},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":163,"substance":98,"depth":97,"authority":163,"freshness":13,"relevant":21,"comment":164},12,"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。",[166],{"name":159,"url":156},[168,169,170,26,171],"智慧农业","农业人工智能","粮食安全","气候分析",{"code":116,"slug":173,"name":174,"path":175,"parent_code":176},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":179,"locked":14,"version":34,"method":35,"evidence":180},"classified",{"candidates":181,"agricultural_evidence":41},[182,187,193,198,203],{"code":116,"score":20,"supported":41,"title":183,"abstract":185,"tags":186},[184],"artificial intelligence",[],[169],{"code":188,"score":117,"supported":41,"title":189,"abstract":190,"tags":192},"I5",[],[191],"农业传感",[],{"code":194,"score":21,"supported":14,"title":195,"abstract":196,"tags":197},"A8",[],[],[170],{"code":199,"score":21,"supported":14,"title":200,"abstract":201,"tags":202},"I1",[],[],[168],{"code":39,"score":21,"supported":14,"title":204,"abstract":205,"tags":206},[],[],[26],[208,209],"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":211,"openalex_id":213,"authors":214,"venue":159,"cited_by_count":74,"oa_url":156,"card":217,"direction":222,"ingested_from":82},"W7223111766",[215],{"name":216,"orcid":9},"Fatima-Zahra El Khattabi",{"tldr":218,"method":219,"finding":220,"direction":80,"opportunity":221},"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":225,"title":226,"url":227,"summary":228,"summary_zh":9,"content":9,"source_name":229,"source_url":227,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":230,"score_detail":231,"sources":233,"tags":235,"primary_topic":239,"secondary_topics":243,"classification":244,"search_phrases":257,"slug":260,"view_count":74,"doi":261,"paper":262,"created_at":278},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","Research Square",38,{"impact":17,"substance":85,"depth":17,"authority":85,"freshness":13,"relevant":21,"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":229,"url":227},[236,237,26,238],"数字农业","农业信息化","地图评估",{"code":199,"slug":240,"name":241,"path":242,"parent_code":176},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":179,"locked":14,"version":34,"method":35,"evidence":245},{"candidates":246,"agricultural_evidence":41},[247,253],{"code":199,"score":248,"supported":41,"title":249,"abstract":251,"tags":252},13,[250],"digital agriculture",[250],[237,236],{"code":39,"score":21,"supported":14,"title":254,"abstract":255,"tags":256},[],[],[26],[258,259],"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":261,"openalex_id":263,"authors":264,"venue":229,"cited_by_count":74,"oa_url":227,"card":9,"direction":277,"ingested_from":82},"W7223099987",[265,268,271,274],{"name":266,"orcid":267},"Phillip Lanza","https:\u002F\u002Forcid.org\u002F0000-0002-5545-1744",{"name":269,"orcid":270},"David G. Rossiter","https:\u002F\u002Forcid.org\u002F0000-0003-4558-1286",{"name":272,"orcid":273},"Diana Stuart Sinton","https:\u002F\u002Forcid.org\u002F0000-0001-5828-9001",{"name":275,"orcid":276},"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":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":287,"sources":289,"tags":291,"primary_topic":9,"secondary_topics":294,"classification":295,"search_phrases":303,"slug":306,"view_count":74,"doi":307,"paper":308,"created_at":323},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":74,"substance":74,"depth":74,"authority":74,"freshness":74,"relevant":74,"comment":288},"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。",[290],{"name":285,"url":282},[26,292,293],"光谱数据","自动分类",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":296},{"candidates":297,"agricultural_evidence":14,"reason":51},[298],{"code":39,"score":299,"supported":41,"title":300,"abstract":301,"tags":302},15,[44],[26,44],[26],[304,305],"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":307,"openalex_id":309,"authors":310,"venue":285,"cited_by_count":74,"oa_url":317,"card":318,"direction":80,"ingested_from":82},"W7223058937",[311,314],{"name":312,"orcid":313},"Aleksandr V. Lapko","https:\u002F\u002Forcid.org\u002F0000-0002-0664-3870",{"name":315,"orcid":316},"Vasiliy A. Lapko","https:\u002F\u002Forcid.org\u002F0000-0001-6938-9323","https:\u002F\u002Fjournals.eco-vector.com\u002F2712-8970\u002Farticle\u002Fdownload\u002F719886\u002F228882",{"tldr":319,"method":320,"finding":321,"direction":80,"opportunity":322},"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":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":331,"score_detail":332,"sources":334,"tags":336,"primary_topic":341,"secondary_topics":346,"classification":347,"search_phrases":377,"slug":380,"view_count":74,"doi":381,"paper":382,"created_at":395},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":17,"substance":97,"depth":99,"authority":163,"freshness":20,"relevant":21,"comment":333},"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。",[335],{"name":330,"url":327},[26,337,338,339,340],"土地利用","土壤侵蚀","GIS","水土保持",{"code":342,"slug":343,"name":344,"path":345,"parent_code":176},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":179,"locked":14,"version":34,"method":35,"evidence":348},{"candidates":349,"agricultural_evidence":41},[350,356,360,367,373],{"code":342,"score":13,"supported":41,"title":351,"abstract":352,"tags":355},[],[353,339,354],"地理信息","geographic information",[339],{"code":39,"score":40,"supported":41,"title":357,"abstract":358,"tags":359},[],[26,44],[26],{"code":361,"score":85,"supported":41,"title":362,"abstract":363,"tags":366},"H2",[],[364,365],"土壤","soil",[],{"code":368,"score":117,"supported":41,"title":369,"abstract":370,"tags":372},"H3",[],[371],"灌溉",[],{"code":47,"score":56,"supported":14,"title":374,"abstract":375,"tags":376},[],[],[337,340],[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":330,"cited_by_count":74,"oa_url":9,"card":390,"direction":80,"ingested_from":82},"W7223117733",[385,387],{"name":386,"orcid":9},"MERİÇ KURT",{"name":388,"orcid":389},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":391,"method":392,"finding":393,"direction":151,"opportunity":394},"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。","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":397,"title":398,"url":399,"summary":400,"summary_zh":401,"content":9,"source_name":402,"source_url":399,"published_at":160,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":403,"score_detail":404,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":415,"classification":416,"search_phrases":433,"slug":436,"view_count":74,"doi":437,"paper":438,"created_at":457},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":163,"substance":18,"depth":19,"authority":99,"freshness":13,"relevant":21,"comment":405},"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。",[407],{"name":402,"url":399},[168,409,26,410],"作物制图","SAR",{"code":39,"slug":412,"name":413,"path":414,"parent_code":176},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":179,"locked":14,"version":34,"method":35,"evidence":417},{"candidates":418,"agricultural_evidence":41},[419,424,429],{"code":39,"score":13,"supported":41,"title":420,"abstract":421,"tags":423},[],[26,44,422],"multispectral",[26],{"code":194,"score":85,"supported":41,"title":425,"abstract":426,"tags":428},[],[170,427],"food security",[],{"code":199,"score":21,"supported":14,"title":430,"abstract":431,"tags":432},[],[],[168],[434,435],"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":437,"openalex_id":439,"authors":440,"venue":402,"cited_by_count":74,"oa_url":399,"card":452,"direction":80,"ingested_from":82},"W7223117203",[441,444,446,449],{"name":442,"orcid":443},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":445,"orcid":9},"Wentao Lv",{"name":447,"orcid":448},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":450,"orcid":451},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":453,"method":454,"finding":455,"direction":80,"opportunity":456},"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"]