[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4519":3,"related-4519":81},{"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":55,"slug":58,"view_count":21,"doi":59,"paper":60,"created_at":80},4519,"Predicting water quality in mine-affected environments: A hyperspectral remote sensing study in the Iberian pyrite Belt","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.srs.2026.100516","Monitoring water quality in Acid Mine Drainage (AMD) environments requires high-resolution data to capture the complex interplay between geochemical and hydrological dynamics. This study integrates unmanned aerial vehicle (UAV)-based hyperspectral imaging with machine learning to estimate pH, sulfate (SO 4 2- ), and iron (Fe) concentrations across nine water bodies at the Tharsis and Lagunazo abandoned mining sites in the Iberian Pyrite Belt (SW Iberian Peninsula), which span a broad hydrochemical gradient. Using in situ measurements for training, five algorithms: Random Forest (RF), k-nearest neighbors (KNN), Partial Least Squares Regression (PLSR), XGBoost, and Multilayer Perceptron (MLP), against dimensionality reduction techniques (LASSO and PCA) were evaluated. The MLP model using the full spectral set yielded the best performance for pH (R 2 = 0.952), while RF models using LASSO-selected features performed best for Fe (R 2 = 0.861) and SO 4 2- (R 2 = 0.880). SHapley Additive exPlanations (SHAP) confirmed that model decisions align with known spectral absorption features. Building on previous hyperspectral AMD studies, this work advances the state of the art by (i) exploiting UAV-scale hyperspectral data to resolve fine-scale spatial variability within individual water bodies, (ii) explicitly distinguishing between optically active and non-optically active water quality parameters, and (iii) systematically comparing multiple machine learning regression algorithms to retrieve pH, iron, and sulfate across chemically diverse systems. By integrating high-resolution imagery with hydrochemical data, this study provides a robust framework for monitoring AMD-impacted surface waters at spatial scales relevant for environmental management.","在酸性矿山排水（AMD）环境中监测水质需要高分辨率数据，以捕捉地球化学过程与水文动态之间复杂的相互作用。本研究将基于无人机（UAV）的高光谱成像与机器学习相结合，估算了伊比利亚黄铁矿带（伊比利亚半岛西南部）Tharsis和Lagunazo废弃矿区九个水体的pH、硫酸盐（SO₄²⁻）和铁（Fe）浓度，这些水体跨越了广泛的水化学梯度。利用原位测量数据进行训练，评估了五种算法：随机森林（RF）、k近邻（KNN）、偏最小二乘回归（PLSR）、XGBoost和多层感知机（MLP），并结合降维技术（LASSO和PCA）进行比较。使用全光谱集的MLP模型在pH估算中表现最佳（R² = 0.952），而使用LASSO筛选特征的RF模型在Fe（R² = 0.861）和SO₄²⁻（R² = 0.880）估算中表现最优。SHapley加法解释（SHAP）证实模型决策与已知的光谱吸收特征相一致。在以往高光谱AMD研究的基础上，本工作通过以下方面推进了该领域的技术水平：（i）利用无人机尺度的高光谱数据解析单个水体内部的精细空间变异性，（ii）明确区分光学活性与非光学活性水质参数，以及（iii）系统比较多种机器学习回归算法以反演化学组成多样系统中的pH、铁和硫酸盐。通过将高分辨率影像与水化学数据相结合，本研究为在与环境管理相关的空间尺度上监测受AMD影响的地表水提供了一个稳健的框架。",null,"Science of Remote Sensing","2026-10-06T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,6,1,"该研究将无人机高光谱与机器学习结合用于酸性矿山废水水质反演，方法新颖、数据扎实，对矿区农业水环境监测具有参考价值，但属细分领域国际论文，公共影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"机器学习","无人机遥感","遥感监测","水质监测","矿山生态修复",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":54},[38,47],{"code":39,"score":40,"supported":41,"title":42,"abstract":45,"tags":46},"I3",21,true,[43,44],"remote sensing","hyperspectral",[44],[27,28],{"code":48,"score":49,"supported":41,"title":50,"abstract":51,"tags":53},"I2",7,[],[26,52],"machine learning",[26],"农业相关性需要复核",[56,57],"伊比利亚黄铁矿带 酸性矿山废水 高光谱","UAV 高光谱 水质反演","伊比利亚黄铁矿带酸性矿山废水高光谱-4519","10.1016\u002Fj.srs.2026.100516",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":71,"oa_url":72,"card":73,"direction":77,"ingested_from":79},"W7220716726",[63,65,68],{"name":64,"orcid":9},"Melisa Isgró",{"name":66,"orcid":67},"Maria  Dolores Basallote","https:\u002F\u002Forcid.org\u002F0000-0003-2011-3806",{"name":69,"orcid":70},"L. Barbero","https:\u002F\u002Forcid.org\u002F0000-0002-3513-2025",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2666017226001549\u002Fpdf",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"用无人机高光谱与机器学习反演矿区酸性废水pH、硫酸盐和铁浓度。","无人机高光谱成像+5种机器学习算法，结合LASSO\u002FPCA降维与SHAP解释。","MLP对pH最佳(R²=0.952)，RF结合LASSO对Fe和SO₄²⁻最佳。","农业遥感与作物表型","可将该高光谱-机器学习框架迁移到农业面源污染水体监测，并探索跨区域迁移学习。","openalex","2026-10-09T23:30:30.251656Z",{"total":20,"page":21,"page_size":20,"items":82},[83,173,270,339,406,454],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":94,"tags":96,"primary_topic":99,"secondary_topics":104,"classification":105,"search_phrases":132,"slug":135,"view_count":136,"doi":137,"paper":138,"created_at":172},2657,"Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183185","Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R2 = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L−1; MARE = 5%) and TP (R2 = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L−1; MARE = 8.7%), whereas XGB performed best for TU (R2 = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R2 = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L−1; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p \u003C 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p \u003C 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures.","监测水质对于保护淡水生态系统和支撑可持续水资源管理至关重要。埃塞俄比亚最大的淡水湖——塔纳湖（Lake Tana）面临着日益加剧的农业和城市压力，而传统监测手段仍然成本高昂且受空间限制。本研究开发了一套集成Sentinel-2遥感与机器学习的框架，利用858个原位观测数据和Google Earth Engine估算叶绿素a（Chl-a）、浊度（TU）、总氮（TN）和总磷（TP）。采用光谱波段、波段组合和指数，评估了随机森林（RF）、极端梯度提升（XGB）、人工神经网络（ANN）和支持向量回归（SVR）的性能。RF对Chl-a（R2 = 0.94 ± 0.01；RMSE = 2.11 ± 0.18 µg L−1；MARE = 5%）和TP（R2 = 0.91 ± 0.01；RMSE = 0.26 ± 0.01 mg L−1；MARE = 8.7%）的预测效果最佳，而XGB对TU（R2 = 0.93 ± 0.01；RMSE = 5.17 ± 0.43 NTU；MARE = 7%）和TN（R2 = 0.94 ± 0.02；RMSE = 0.18 ± 0.02 mg L−1；MARE = 9.9%）的预测效果最佳。RF和XGB在光学活性和非光学活性参数上均表现出强大的预测性能，表明该框架能够捕捉复杂的光谱水质关系并支持空间连续评估。显著的季节性差异（p \u003C 0.001）显示旱季Chl-a较高（137.1%），雨季TP（21.7%）、TU（7.5%）和TN（3.9%）较高。长期配对观测进一步表明，从2016年12月至2025年12月，Chl-a（73.7%）、TN（30%）和TP（14.3%）均有所增加（p \u003C 0.001）。空间热点分析揭示了TU、TN和TP的强烈聚集性，尤其是在支流河口和近岸区域，凸显了优先监测和干预区域。总体而言，整合实地观测、Sentinel-2影像和机器学习为监测多种水质参数提供了一种准确、可扩展且具有成本效益的方法。该框架为加强数据稀缺地区的淡水监测以及支持在水质压力日益增大背景下湖泊的可持续管理提供了一种可迁移的解决方案。","Remote Sensing","2026-09-16T00:00:00Z",82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":93},"基于Sentinel-2与机器学习实现湖泊多参数水质反演，方法可迁移至国内农业面源污染与渔业水域监测，数据规模与精度均具参考价值。",[95],{"name":89,"url":86},[26,97,98,28,29],"农业面源污染","智慧渔业",{"code":100,"slug":101,"name":102,"path":103,"parent_code":9},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":106,"locked":14,"version":34,"method":35,"evidence":107},"partial",{"candidates":108,"agricultural_evidence":41,"reason":131},[109,114,119,126],{"code":48,"score":110,"supported":41,"title":111,"abstract":112,"tags":113},15,[52],[26,52],[26],{"code":39,"score":110,"supported":41,"title":115,"abstract":116,"tags":118},[43],[117,43],"遥感",[28],{"code":120,"score":20,"supported":41,"title":121,"abstract":122,"tags":125},"H3",[],[123,124],"水资源","water resource",[],{"code":127,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},"H5",[],[],[97],"一级领域明确，二级方向需要细分",[133,134],"农业面源污染 智慧渔业 机器学习 水质监测","农业面源污染 智慧渔业","农业面源污染智慧渔业机器学习水质监测-2657",2,"10.3390\u002Frs18183185",{"doi":137,"openalex_id":139,"authors":140,"venue":89,"cited_by_count":71,"oa_url":86,"card":167,"direction":77,"ingested_from":79},"W7213230686",[141,144,146,149,151,154,156,158,160,162,164],{"name":142,"orcid":143},"Lakachew Y. Alemneh","https:\u002F\u002Forcid.org\u002F0009-0004-3471-3778",{"name":145,"orcid":9},"Daganchew Aklog",{"name":147,"orcid":148},"Ann van Griensven","https:\u002F\u002Forcid.org\u002F0000-0002-2105-6287",{"name":150,"orcid":9},"Minychl G. Dersseh",{"name":152,"orcid":153},"Goraw Goshu","https:\u002F\u002Forcid.org\u002F0000-0001-9629-0126",{"name":155,"orcid":9},"Seleshi Yalew",{"name":157,"orcid":9},"Demesew A. Mhiret",{"name":159,"orcid":9},"Sisay B. Asress",{"name":161,"orcid":9},"Tigistu Wassie Agegnehu",{"name":163,"orcid":9},"Shawl Abebe Desta",{"name":165,"orcid":166},"Samuel Berihun Kassa","https:\u002F\u002Forcid.org\u002F0009-0004-5618-9743",{"tldr":168,"method":169,"finding":170,"direction":77,"opportunity":171},"用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。","Sentinel-2影像、Google Earth Engine、858个实测点","RF与XGB精度最高（R²达0.91-0.94），水质参数呈显著季节与年际上升趋势。","可迁移至其他数据稀缺湖泊，探索水葫芦覆盖下水体光谱混合与多源遥感协同反演。","2026-09-16T23:30:28.415573Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":187,"tags":189,"primary_topic":193,"secondary_topics":199,"classification":210,"search_phrases":246,"slug":249,"view_count":71,"doi":250,"paper":251,"created_at":269},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":183,"substance":40,"depth":184,"authority":185,"freshness":13,"relevant":21,"comment":186},16,17,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[188],{"name":179,"url":176},[190,26,28,191,192],"智慧农业","土壤墒情","冬小麦",{"code":194,"slug":195,"name":196,"path":197,"parent_code":198},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[200,206],{"code":201,"slug":202,"name":203,"path":204,"parent_code":205},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":48,"slug":207,"name":208,"path":209,"parent_code":100},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":211,"locked":14,"version":34,"method":35,"evidence":212},"classified",{"candidates":213,"agricultural_evidence":41},[214,220,225,229,234,240],{"code":194,"score":215,"supported":41,"title":216,"abstract":218,"tags":219},26,[217,195],"winter wheat",[196,217,195],[192],{"code":201,"score":110,"supported":41,"title":221,"abstract":222,"tags":224},[202],[223,202],"土壤",[191],{"code":48,"score":110,"supported":41,"title":226,"abstract":227,"tags":228},[52],[26,52],[26],{"code":39,"score":13,"supported":41,"title":230,"abstract":231,"tags":233},[],[117,43,232],"multispectral",[28],{"code":120,"score":20,"supported":41,"title":235,"abstract":236,"tags":239},[],[237,238],"灌溉","irrigation",[],{"code":241,"score":242,"supported":41,"title":243,"abstract":244,"tags":245},"I1",4,[],[190],[190],[247,248],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":250,"openalex_id":252,"authors":253,"venue":179,"cited_by_count":71,"oa_url":176,"card":263,"direction":268,"ingested_from":79},"W7223156204",[254,257,259,261],{"name":255,"orcid":256},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":258,"orcid":9},"Yi Zhang",{"name":260,"orcid":9},"WEI Lei",{"name":262,"orcid":9},"Chengcai Zhang",{"tldr":264,"method":265,"finding":266,"direction":77,"opportunity":267},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":278,"sources":280,"tags":282,"primary_topic":285,"secondary_topics":286,"classification":292,"search_phrases":316,"slug":319,"view_count":71,"doi":320,"paper":321,"created_at":338},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":71,"substance":71,"depth":71,"authority":71,"freshness":71,"relevant":71,"comment":279},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[281],{"name":276,"url":273},[26,28,283,284],"土地利用","城市扩张",{"code":48,"slug":207,"name":208,"path":209,"parent_code":100},[287],{"code":288,"slug":289,"name":290,"path":291,"parent_code":100},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":211,"locked":14,"version":34,"method":35,"evidence":293},{"candidates":294,"agricultural_evidence":41},[295,299,307,311],{"code":48,"score":110,"supported":41,"title":296,"abstract":297,"tags":298},[52],[26,52],[26],{"code":288,"score":300,"supported":41,"title":301,"abstract":302,"tags":306},9,[],[303,304,305],"空间分析","GIS","spatial analysis",[],{"code":39,"score":49,"supported":41,"title":308,"abstract":309,"tags":310},[],[117,43],[28],{"code":312,"score":21,"supported":14,"title":313,"abstract":314,"tags":315},"H6",[],[],[283],[317,318],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":320,"openalex_id":322,"authors":323,"venue":276,"cited_by_count":71,"oa_url":273,"card":333,"direction":77,"ingested_from":79},"W7223022155",[324,327,329,331],{"name":325,"orcid":326},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":328,"orcid":9},"Goanar Jal Dup",{"name":330,"orcid":9},"Weyesa Merga Chemeda",{"name":332,"orcid":9},"Shankar Karuppannan",{"tldr":334,"method":335,"finding":336,"direction":77,"opportunity":337},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":345,"source_url":342,"published_at":346,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":71,"score_detail":347,"sources":349,"tags":351,"primary_topic":9,"secondary_topics":354,"classification":355,"search_phrases":381,"slug":384,"view_count":71,"doi":385,"paper":386,"created_at":405},5175,"Driving forces of contemporary wildfire severity in the American Southwest: a remote sensing and machine learning case study in Arizona, USA","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20964471.2026.2735134","As climate change continues to intensify atmospheric moisture demand across the American Southwest, predicting where wildfires cause severe vegetation mortality remains a major challenge. New thermal and optical harmonized satellite products provide emerging opportunities for evaluating how pre-fire vegetation moisture and water stress influence fire severity compared to other variables. We modeled continuous Relativized differenced Normalized Burn Ratio (RdNBR) for 45 wildfires that burned in the 2019 and 2020 fire seasons using random forest to compare Harmonized Landsat–Sentinel-2 vegetation indices, ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) evapotranspiration and evaporative-stress metrics, Daymet pre-fire weather variables, LANDFIRE fuels attributes, and topographic predictors across five cumulative pre-ignition windows from 1 week to 3 months before ignition. Despite exceptional drought and anomalous fire-weather conditions, low-severity fire was common over the study period. Integrated models outperformed individual product-family models, indicating that fire severity was best explained by combined antecedent weather, terrain, fuels, vegetation type, and vegetation-moisture predictors. Temperature, elevation, solar radiation, vapor pressure, and HLS Normalized Difference Moisture Index (NDMI) were among the most consistent predictors. NDMI was the most consistent HLS plant-moisture metric, but vegetation moisture alone did not explain fire severity. Canopy cover was also important in woodlands and forests. Models using data from shorter pre-ignition windows outperformed longer pre-fire window models. They varied in performance among vegetation types, with the strongest vegetation-specific performance in forests and woodlands. These findings show that emerging high-resolution satellite products can help characterize pre-fire vegetation conditions and improve fire-risk assessments, especially when integrated with short-term antecedent weather, landscape moisture, and terrain data.","随着气候变化持续加剧美国西南部的大气水分需求，预测野火在何处导致严重植被死亡仍是一项重大挑战。新的热红外与光学协调卫星产品为评估火灾前植被水分和水分胁迫相较于其他变量如何影响火烈度提供了新的机遇。我们利用随机森林对2019年和2020年火季燃烧的45场野火构建了连续相对差分归一化燃烧比（RdNBR）模型，比较了协调陆地卫星—哨兵2号（Harmonized Landsat–Sentinel-2）植被指数、ECOSTRESS（国际空间站生态系统空间热辐射计实验）蒸散发与蒸发胁迫指标、Daymet火前气象变量、LANDFIRE燃料属性以及地形预测因子，并考察了从点火前1周至3个月的五个累积点火前时间窗口。尽管期间出现了极端干旱和异常火险天气条件，研究期内低烈度火仍较为普遍。集成模型的表現优于单一产品类别模型，表明火烈度最好由前期气象、地形、燃料、植被类型和植被水分预测因子的组合来解释。温度、海拔、太阳辐射、水汽压和HLS归一化差异水分指数（NDMI）是最为一致的预测因子。NDMI是最一致的HLS植物水分指标，但仅凭植被水分并不能解释火烈度。冠层覆盖度在疏林和林地中也很重要。使用较短点火前时间窗口数据的模型优于较长火前窗口模型。不同植被类型的模型表现存在差异，其中森林和林地的植被特异性表现最强。这些发现表明，新兴高分辨率卫星产品有助于刻画火前植被状况并改进火险评估，尤其是在与短期前期气象、景观水分和地形数据集成时。","Big Earth Data","2026-10-15T00:00:00Z",{"impact":71,"substance":71,"depth":71,"authority":71,"freshness":71,"relevant":71,"comment":348},"该研究聚焦美国西南部野火严重程度的遥感与机器学习建模，属于生态与灾害遥感领域，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。",[350],{"name":345,"url":342},[26,28,352,353],"野火风险","植被水分",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":356},{"candidates":357,"agricultural_evidence":14,"reason":54},[358,367,371,375],{"code":359,"score":13,"supported":41,"title":360,"abstract":361,"tags":366},"H7",[],[362,363,364,365],"气候变化","干旱","climate change","drought",[352],{"code":48,"score":300,"supported":41,"title":368,"abstract":369,"tags":370},[52],[],[26],{"code":39,"score":300,"supported":41,"title":372,"abstract":373,"tags":374},[43],[],[28],{"code":120,"score":20,"supported":41,"title":376,"abstract":377,"tags":380},[],[378,379],"蒸散发","evapotranspiration",[],[382,383],"美国西南部 野火 遥感","ECOSTRESS 植被水分 火险","美国西南部野火遥感-5175","10.1080\u002F20964471.2026.2735134",{"doi":385,"openalex_id":387,"authors":388,"venue":345,"cited_by_count":71,"oa_url":399,"card":400,"direction":77,"ingested_from":79},"W7222006390",[389,392,394,396],{"name":390,"orcid":391},"Helen Mills Poulos","https:\u002F\u002Forcid.org\u002F0000-0003-0686-9770",{"name":393,"orcid":9},"Sarah Hardy",{"name":395,"orcid":9},"Jenna Lynne Otaola",{"name":397,"orcid":398},"Andrew  Marder Barton","https:\u002F\u002Forcid.org\u002F0000-0001-5978-0337","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F20964471.2026.2735134?needAccess=true",{"tldr":401,"method":402,"finding":403,"direction":77,"opportunity":404},"用遥感与随机森林分析亚利桑那45场野火，识别火前植被水分等驱动因子。","HLS植被指数、ECOSTRESS蒸散、Daymet气象与地形数据，随机森林建模","火险严重度需综合火前短期天气、地形、燃料与植被水分，单一水分指标不足。","可借鉴其多源遥感融合与短窗口建模思路，研究作物干旱胁迫与灾损风险预警。","2026-10-17T23:30:30.060308Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":419,"tags":421,"primary_topic":9,"secondary_topics":425,"classification":426,"search_phrases":434,"slug":437,"view_count":71,"doi":438,"paper":439,"created_at":453},5100,"Scale‐space image analysis for mapping spotted knapweed in grassland ecosystems","https:\u002F\u002Fdoi.org\u002F10.1002\u002Faps3.70086","Abstract Premise This study investigates the value of scale‐space representations of spatial features for the identification and mapping of invasive plants using high‐resolution multispectral imagery. Scale‐space representations combine the two‐dimensional spatial domain with a third dimension representing spatial scale, such that features can be represented across both space and image spatial resolution. Methods Gaussian pyramids were constructed to create representations of the features across five image resolution levels (between 3 and 46 cm) within the scale space. A case study was employed to evaluate the importance of features across the scale space for the identification and mapping of spotted knapweed in a grassland ecosystem using multispectral imagery acquired using a remotely piloted aircraft system (RPAS). Classification was performed using a random forest classifier that was constructed using a process that included feature and hyperparameter optimization. Results The results of this case study show that classifiers constructed with only high spatial resolution features were associated with better classification performance than those constructed with only lower spatial resolution features; however, the best classification is achieved by leveraging features from throughout the feature scale spaces. Discussion These findings suggest that optimization of the spatial scale at which features are computed can substantially improve remote sensing image analysis and potentially inform RPAS flight planning to improve mission efficiency.","摘要 前提 本研究探讨了空间特征的尺度空间表示在高分辨率多光谱影像中用于入侵植物识别与制图的价值。尺度空间表示将二维空间域与表示空间尺度的第三维相结合，从而使特征能够在空间和影像空间分辨率两个维度上得到表示。方法 构建高斯金字塔，以在尺度空间内生成跨越五个影像分辨率层级（3至46厘米）的特征表示。通过一个案例研究，评估尺度空间中的特征在使用由遥控驾驶航空器系统（RPAS）获取的多光谱影像对草地生态系统中斑点矢车菊进行识别与制图时的重要性。分类使用随机森林分类器完成，该分类器通过包含特征优化和超参数优化的流程构建。结果 本案例研究的结果表明，仅使用高空间分辨率特征构建的分类器，其分类性能优于仅使用较低空间分辨率特征构建的分类器；然而，最佳分类效果是通过综合利用整个特征尺度空间中的特征实现的。讨论 这些发现表明，优化特征计算所采用的空间尺度可以显著改进遥感影像分析，并有可能为RPAS飞行规划提供依据，以提高任务效率。","Applications in Plant Sciences","2026-10-14T00:00:00Z",71,{"impact":416,"substance":40,"depth":184,"authority":185,"freshness":417,"relevant":21,"comment":418},12,8,"方法新颖、结论可靠，但属细分领域基础研究，产业影响有限，适合作为技术前沿收录而非头条精选。",[420],{"name":412,"url":409},[27,28,422,423,424],"多光谱影像","入侵植物","草地生态",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":427},{"candidates":428,"agricultural_evidence":14,"reason":54},[429],{"code":39,"score":430,"supported":41,"title":431,"abstract":432,"tags":433},11,[],[117,43,232],[27,28],[435,436],"斑点矢车菊 无人机 遥感","尺度空间 多光谱 草地","斑点矢车菊无人机遥感-5100","10.1002\u002Faps3.70086",{"doi":438,"openalex_id":440,"authors":441,"venue":412,"cited_by_count":71,"oa_url":447,"card":448,"direction":77,"ingested_from":79},"W7222056923",[442,444],{"name":443,"orcid":9},"Shohreh Sahebi",{"name":445,"orcid":446},"David Hill","https:\u002F\u002Forcid.org\u002F0000-0002-4530-3131","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Faps3.70086",{"tldr":449,"method":450,"finding":451,"direction":77,"opportunity":452},"利用尺度空间表示多光谱影像特征，结合随机森林分类器，实现草地生态系统中斑点矢车菊的识别与制图。","构建高斯金字塔生成5个分辨率层级（3-46cm）的尺度空间特征，用随机森林分类并","仅用高分辨率特征的分类器优于仅用低分辨率特征，但融合整个尺度空间特征时分类效果最佳。","可探索尺度空间特征优化在入侵植物早期监测中的应用，并指导无人机飞行规划以提升效率。","2026-10-16T23:30:31.287501Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":466,"tags":468,"primary_topic":471,"secondary_topics":476,"classification":477,"search_phrases":514,"slug":517,"view_count":71,"doi":9,"paper":518,"created_at":535},4890,"ระบบพยากรณและเฝาระวงคณภาพนำอจฉรยะตามดชนคณภาพนำ ดวยเทคนคการเรยนรของเครองและอนเทอรเนตของสรรพสง","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FJSSE\u002Farticle\u002Fview\u002F289948","This research utilizes Internet of Things (IoT) technology and Machine Learning (ML) to develop a prototype for a smart water quality forecasting and monitoring system. The study focuses on analyzing the Reduced Water Quality Index (rWQI) based on water temperature, pH, and Total Dissolved Solids (TDS). To forecast water quality 1 to 6 hours in advance, the performance of four predictive models was evaluated and compared: Extremely Randomized Trees (ET), Random Forest (RF), Simple Linear Regression (LR), and Multiple Linear Regression (MLR). The efficiency of these algorithms was validated using four key evaluation metrics: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE). The experimental results indicated that the predictive performance of the models varied significantly depending on the forecasting horizon. In the short-term horizon (1–3 hours ahead), the RF model achieved the highest performance, with an R² of 0.566 and an MAE of 5.215 in the first hour. Conversely, for long-term forecasting (4–6 hours ahead), all models exhibited a significant drop in performance (R² \u003C 0), yielding higher prediction errors than the Mean Baseline. This long-term limitation stems from high water quality volatility in real-world environments combined with error accumulation in multi-step ahead forecasting. In conclusion, these findings emphasize that aligning model selection with the specific forecasting horizon is critical for enhancing system efficiency and stability. The developed model demonstrates high accuracy and suitability for short-term early warnings within 3 hours. This knowledge enables operators to plan water quality management with precision and speed, thereby reducing operational costs and the risk of aquatic livestock loss. Furthermore, this system serves as a technological prototype in digital agriculture, elevating conventional water quality monitoring into proactive management through intelligent technology.","本研究利用物联网（IoT）技术与机器学习（ML），开发了一套智能水质预测与监测系统原型。研究聚焦于基于水温、pH值和总溶解固体（TDS）的简化水质指数（rWQI）分析。为提前1至6小时预测水质，本研究评估并比较了四种预测模型的性能：极端随机树（ET）、随机森林（RF）、简单线性回归（LR）和多元线性回归（MLR）。采用四项关键评价指标验证了这些算法的有效性：决定系数（R²）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和对称平均绝对百分比误差（sMAPE）。实验结果表明，各模型的预测性能因预测时间跨度不同而存在显著差异。在短期预测（提前1至3小时）中，RF模型表现最佳，第一小时的R²为0.566，MAE为5.215。相反，在长期预测（提前4至6小时）中，所有模型性能均显著下降（R² \u003C 0），预测误差高于均值基线。这一长期局限性源于真实环境中水质的高波动性以及多步超前预测中的误差累积。综上所述，这些发现强调，将模型选择与特定预测时间跨度相匹配对于提升系统效率与稳定性至关重要。所开发的模型具有较高精度，适用于3小时内的短期预警。这一认识使操作人员能够精准、快速地规划水质管理，从而降低运营成本和水产养殖损失风险。此外，该系统可作为数字农业中的技术原型，通过智能技术将传统水质监测提升为主动管理。","Journal of Science and Science Education (JSSE)","2026-10-12T00:00:00Z",70,{"impact":416,"substance":464,"depth":184,"authority":185,"freshness":417,"relevant":21,"comment":465},20,"论文将物联网与机器学习结合用于水产养殖水质短期预警，方法对比扎实、结论明确，但属细分技术原型，影响范围有限。",[467],{"name":460,"url":457},[190,26,469,470,29],"物联网","水产养殖",{"code":472,"slug":473,"name":474,"path":475,"parent_code":100},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[],{"status":211,"locked":14,"version":34,"method":35,"evidence":478},{"candidates":479,"agricultural_evidence":41},[480,486,492,496,502,509],{"code":472,"score":13,"supported":41,"title":481,"abstract":482,"tags":485},[],[469,483,484],"internet of things","IoT",[469],{"code":241,"score":49,"supported":41,"title":487,"abstract":488,"tags":491},[],[489,490],"数字农业","digital agriculture",[190],{"code":48,"score":49,"supported":41,"title":493,"abstract":494,"tags":495},[],[26,52],[26],{"code":497,"score":242,"supported":41,"title":498,"abstract":499,"tags":501},"D",[],[500],"水产",[470],{"code":503,"score":504,"supported":41,"title":505,"abstract":506,"tags":508},"C",3,[],[507],"livestock",[],{"code":510,"score":21,"supported":14,"title":511,"abstract":512,"tags":513},"D4",[],[],[29],[515,516],"物联网 机器学习 水质预测","rWQI 水质监测 水产养殖","物联网机器学习水质预测-4890",{"doi":9,"openalex_id":519,"authors":520,"venue":460,"cited_by_count":71,"oa_url":457,"card":530,"direction":268,"ingested_from":79},"W7221732805",[521,523,525,527],{"name":522,"orcid":9},"Wanarase Sinhashthita",{"name":524,"orcid":9},"Tossaporn Alherbe",{"name":526,"orcid":9},"Kamol Chareonsri",{"name":528,"orcid":529},"Chayaporn Kaensar","https:\u002F\u002Forcid.org\u002F0009-0005-3330-8532",{"tldr":531,"method":532,"finding":533,"direction":268,"opportunity":534},"利用物联网与机器学习构建水质预测监测系统，提前1-6小时预测rWQI。","基于水温、pH、TDS数据，比较ET、RF、LR、MLR四种模型预测性能。","短期（1-3小时）RF最优，长期（4-6小时）所有模型性能显著下降。","可探索融合时序深度学习与误差校正，提升长期水质预测精度与鲁棒性。","2026-10-14T23:30:13.314647Z"]