[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1320":3,"related-1320":100},{"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":30,"secondary_topics":36,"classification":37,"search_phrases":67,"slug":70,"view_count":20,"doi":71,"paper":72,"created_at":99},1320,"Quantifying soil erosion rates in tropical drylands: An innovative SBAS InSAR and statistical clustering approach in La Tatacoa, Colombia","https:\u002F\u002Fdoi.org\u002F10.1177\u002F03091333261480928","Quantifying soil erosion in vulnerable tropical drylands is critical for desertification management but remains methodologically challenging due to the spatial limitations of field-based methods. This study introduces a novel framework integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) with unsupervised statistical clustering to quantify erosion rates and validate spatial heterogeneity in the La Tatacoa dry forest, Colombia. Using 123 Sentinel-1 radar images (from 2018 to 2023), we generated high-resolution displacement velocity maps, which were then integrated with Normalized Difference Vegetation Index (NDVI) data from Sentinel-2 and geomorphometric attributes derived from the ALOS PALSAR Digital Elevation Model. Study-wide mean erosion rates ranged from 0.43 to 0.54 mm\u002Fyear; however, these averages masked significant localized instability. Time-series K-means clustering of 100 randomized sampling points identified four distinct deformation regimes, statistically confirming that high-velocity zones are structural realities rather than measurement noise. Specifically, Cluster 4 identified critical hotspots where erosion rates reached 30 mm\u002Fyear exceeding the United States Department of Agriculture (USDA) soil tolerance threshold (0.93 mm\u002Fyear) by a factor of 32. These hotspots are systematically confined to dissected terrain with slopes > 9 ° and NDVI \u003C 0.15 . By rigorously distinguishing genuine deformation regimes from background variability, this framework provides a scalable, evidence-based tool for prioritizing conservation interventions in data-limited dryland ecosystems.","量化脆弱热带旱地的土壤侵蚀对荒漠化治理至关重要，但由于实地方法在空间上的局限性，这一任务在方法论上仍具挑战性。本研究提出了一种新框架，将小基线集合成孔径雷达干涉测量（SBAS-InSAR）与无监督统计聚类相结合，以量化侵蚀速率并验证哥伦比亚拉塔塔科阿干旱森林的空间异质性。利用123幅Sentinel-1雷达影像（2018年至2023年），我们生成了高分辨率位移速度图，并将其与来自Sentinel-2的归一化差异植被指数（NDVI）数据以及源自ALOS PALSAR数字高程模型的地貌计量属性相结合。研究区整体平均侵蚀速率介于0.43至0.54毫米\u002F年之间；然而，这些平均值掩盖了显著的局部不稳定性。对100个随机采样点进行时间序列K均值聚类，识别出四种不同的形变状态，统计上证实高速度区域是结构性现实而非测量噪声。具体而言，聚类4识别出关键热点区域，其侵蚀速率达到30毫米\u002F年，超过美国农业部（USDA）土壤耐受阈值（0.93毫米\u002F年）达32倍。这些热点系统性地局限于坡度大于9°且NDVI小于0.15的切割地形中。通过严格区分真实形变状态与背景变异性，该框架为数据有限的旱地生态系统提供了可扩展、基于证据的优先保护干预工具。",null,"Progress in Physical Geography Earth and Environment","2026-08-31T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,20,18,2,1,"研究提出SBAS-InSAR与聚类结合的新方法，量化热带旱地土壤侵蚀速率，为荒漠化治理提供科学工具，但时效性较低。",[24],{"name":10,"url":6},[26,27,28,29],"生态保护","遥感监测","土壤侵蚀","干旱区",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,57,62],{"code":31,"score":44,"supported":45,"title":46,"abstract":47,"tags":49},14,true,[32],[48,32],"土壤",[],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"H7",4,[],[55],"干旱",[29],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"H6",[],[],[26],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"I3",[],[],[27],[68,69],"土壤侵蚀 生态保护 遥感监测 干旱区","土壤侵蚀 生态保护","土壤侵蚀生态保护遥感监测干旱区-1320","10.1177\u002F03091333261480928",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":90,"oa_url":9,"card":91,"direction":97,"ingested_from":98},"W7204814048",[75,78,81,83,85,88],{"name":76,"orcid":77},"Mateo Sánchez-Ospina","https:\u002F\u002Forcid.org\u002F0009-0006-1370-0542",{"name":79,"orcid":80},"Susana Salazar","https:\u002F\u002Forcid.org\u002F0000-0002-5228-2228",{"name":82,"orcid":9},"Ruben Arismendy",{"name":84,"orcid":9},"Chajid Kairuz",{"name":86,"orcid":87},"Andrés Ochoa","https:\u002F\u002Forcid.org\u002F0000-0002-7230-4623",{"name":89,"orcid":9},"Kenneth Cabrera-Torres",0,{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"提出SBAS-InSAR与统计聚类结合的方法，量化哥伦比亚干旱区土壤侵蚀速率并识别热点区域。","SBAS-InSAR处理123景Sentinel-1影像，结合NDVI和地形因子","平均侵蚀速率0.43-0.54毫米\u002F年，但热点区域达30毫米\u002F年，超过USDA阈值32倍，集中在陡坡","农业遥感与作物表型","该方法可扩展至其他数据稀缺的干旱区，结合高分辨率影像和机器学习，提升侵蚀预测精度，并探索与土壤属性关联。","数字乡村与农业信息化","openalex","2026-09-01T23:30:25.233021Z",{"total":101,"page":21,"page_size":101,"items":102},6,[103,169,251,340,409,490],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":128,"classification":130,"search_phrases":149,"slug":152,"view_count":20,"doi":153,"paper":154,"created_at":168},4365,"An Index-Based Algorithm for Detecting Water Soil Erosion Indicators from Sentinel-2 Multispectral Data and Its Verification on Field Data of Rostov Oblast and Krasnodar Krai","https:\u002F\u002Fdoi.org\u002F10.23947\u002F2587-8999-2026-10-3-49-57","Introduction . The paper presents the development and testing of an index-based algorithm for detecting indicators of soil erosion by water from Sentinel-2 remote sensing data, considered as the first practical stage in constructing a hybrid physics-based mass-transport model with neural-network parameter identification. Materials and Methods . The algorithm relies on joint analysis of time series of the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), followed by spatial change detection (ΔBSI) with threshold filtering at the 90th percentile. The algorithm was first validated on synthetic data and then applied to real Sentinel-2 imagery for two test sites – the Kamensky District of Rostov Oblast and the vicinity of Moldovanskoye village, Krymsky District, Krasnodar Krai – covering 10 dates within 2024. Results. The algorithm is shown to consistently identify spatially localised zones of increasing bare-soil exposure. Discussion . A key limitation – the inability to distinguish erosion-related exposure from ordinary agrotechnical bare soil — is discussed, along with directions for resolving it (digital elevation model overlay, multi-year analysis). Conclusion . An index-based algorithm for detecting indicators of water-induced soil erosion from Sentinel-2 multispectral time series was developed and validated. Verification on synthetic data and application to two real test areas in southern Russia confirmed its operability while demonstrating the need to separate erosion-related changes from agricultural soil exposure in subsequent model development.","引言。本文介绍了一种基于指数的算法开发与测试，该算法用于从Sentinel-2遥感数据中检测水蚀土壤侵蚀指标，被视为构建基于物理的质量输运混合模型并结合神经网络参数辨识的第一个实践阶段。材料与方法。该算法依赖于对归一化植被指数（NDVI）和裸土指数（BSI）时间序列的联合分析，随后进行空间变化检测（ΔBSI），并在第90百分位数处进行阈值滤波。该算法首先在合成数据上进行了验证，然后应用于两个测试点的真实Sentinel-2影像——罗斯托夫州卡缅斯基区和克拉斯诺达尔边疆区克里姆斯克区摩尔多瓦诺夫斯科耶村附近——覆盖2024年内的10个日期。结果。结果表明，该算法能够一致地识别出裸土暴露增加的空间局部区域。讨论。讨论了关键局限性——无法区分与侵蚀相关的裸土暴露和普通农技作业造成的裸土——以及解决该问题的方向（叠加数字高程模型、多年分析）。结论。开发并验证了一种基于指数的算法，用于从Sentinel-2多光谱时间序列中检测水蚀土壤侵蚀指标。在合成数据上的验证以及在俄罗斯南部两个真实测试区域的应用证实了其可操作性，同时表明在后续模型开发中有必要将与侵蚀相关的变化与农业裸土暴露区分开来。","COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES","2026-10-05T00:00:00Z",70,{"impact":17,"substance":18,"depth":113,"authority":114,"freshness":115,"relevant":21,"comment":116},17,13,8,"提出基于NDVI与BSI时序的土壤水蚀指标遥感识别算法，并在俄罗斯南部两个试验区验证，方法有新意但属区域尺度研究，对国内农业遥感监测有参考价值。",[118],{"name":109,"url":106},[120,121,122,27,28],"智慧农业","Sentinel-2","NDVI",{"code":63,"slug":124,"name":125,"path":126,"parent_code":127},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[129],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":131},{"candidates":132,"agricultural_evidence":45},[133,140,144],{"code":63,"score":18,"supported":45,"title":134,"abstract":136,"tags":139},[135],"multispectral",[137,138,135],"遥感","remote sensing",[122,121,27],{"code":31,"score":44,"supported":45,"title":141,"abstract":142,"tags":143},[32],[48,32],[],{"code":145,"score":21,"supported":14,"title":146,"abstract":147,"tags":148},"I1",[],[],[120],[150,151],"Sentinel-2 土壤侵蚀 遥感","罗斯托夫州 克拉斯诺达尔 土壤侵蚀","Sentinel-2土壤侵蚀遥感-4365","10.23947\u002F2587-8999-2026-10-3-49-57",{"doi":153,"openalex_id":155,"authors":156,"venue":109,"cited_by_count":90,"oa_url":106,"card":163,"direction":95,"ingested_from":98},"W7220651513",[157,160],{"name":158,"orcid":159},"V. A. Blagin","https:\u002F\u002Forcid.org\u002F0009-0003-3239-6150",{"name":161,"orcid":162},"V. A. Beraia","https:\u002F\u002Forcid.org\u002F0009-0006-2270-5011",{"tldr":164,"method":165,"finding":166,"direction":95,"opportunity":167},"开发基于Sentinel-2时序NDVI与BSI指数的水土流失指标检测算法，并在俄罗斯南部实地验证。","联合NDVI与BSI时序，用ΔBSI阈值（90百分位）检测裸土变化，合成与真实数","算法能稳定识别裸土暴露增加区域，但无法区分侵蚀与农事裸土，需DEM和多年度分析。","可融合DEM与多年度时序，构建区分侵蚀与农事裸土的分类模型，并接入神经网络参数识别。","2026-10-07T23:30:29.080156Z",{"id":170,"title":171,"url":172,"summary":173,"summary_zh":174,"content":9,"source_name":175,"source_url":172,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":177,"score_detail":178,"sources":183,"tags":185,"primary_topic":190,"secondary_topics":191,"classification":192,"search_phrases":213,"slug":216,"view_count":90,"doi":217,"paper":218,"created_at":250},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA","2026-10-20T00:00:00Z",79,{"impact":179,"substance":180,"depth":19,"authority":44,"freshness":181,"relevant":21,"comment":182},16,22,9,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[184],{"name":175,"url":172},[27,186,187,188,189],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":193},{"candidates":194,"agricultural_evidence":45},[195,199,204,209],{"code":31,"score":44,"supported":45,"title":196,"abstract":197,"tags":198},[32],[48,32],[],{"code":63,"score":200,"supported":45,"title":201,"abstract":202,"tags":203},7,[],[137,138],[27],{"code":51,"score":101,"supported":45,"title":205,"abstract":206,"tags":208},[],[207,55],"气候适应",[],{"code":58,"score":21,"supported":14,"title":210,"abstract":211,"tags":212},[],[],[188],[214,215],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":217,"openalex_id":219,"authors":220,"venue":175,"cited_by_count":90,"oa_url":172,"card":245,"direction":95,"ingested_from":98},"W7223069171",[221,223,225,227,230,232,235,238,240,242],{"name":222,"orcid":9},"Yue Li",{"name":224,"orcid":9},"Mengfei Shi",{"name":226,"orcid":9},"Fei Zhang",{"name":228,"orcid":229},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":231,"orcid":9},"Gowhar Meraj",{"name":233,"orcid":234},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":236,"orcid":237},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":239,"orcid":9},"Yuanfang Chai",{"name":241,"orcid":9},"Hongtao Xu",{"name":243,"orcid":244},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":246,"method":247,"finding":248,"direction":95,"opportunity":249},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z",{"id":252,"title":253,"url":254,"summary":255,"summary_zh":256,"content":9,"source_name":257,"source_url":254,"published_at":258,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":263,"tags":265,"primary_topic":269,"secondary_topics":275,"classification":282,"search_phrases":316,"slug":319,"view_count":90,"doi":320,"paper":321,"created_at":339},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":179,"substance":261,"depth":113,"authority":114,"freshness":13,"relevant":21,"comment":262},21,"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。",[264],{"name":257,"url":254},[120,266,27,267,268],"机器学习","土壤墒情","冬小麦",{"code":270,"slug":271,"name":272,"path":273,"parent_code":274},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[276,277],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":278,"slug":279,"name":280,"path":281,"parent_code":127},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":38,"locked":14,"version":39,"method":40,"evidence":283},{"candidates":284,"agricultural_evidence":45},[285,291,296,301,305,312],{"code":270,"score":286,"supported":45,"title":287,"abstract":289,"tags":290},26,[288,271],"winter wheat",[272,288,271],[268],{"code":31,"score":292,"supported":45,"title":293,"abstract":294,"tags":295},15,[32],[48,32],[267],{"code":278,"score":292,"supported":45,"title":297,"abstract":299,"tags":300},[298],"machine learning",[266,298],[266],{"code":63,"score":13,"supported":45,"title":302,"abstract":303,"tags":304},[],[137,138,135],[27],{"code":306,"score":101,"supported":45,"title":307,"abstract":308,"tags":311},"H3",[],[309,310],"灌溉","irrigation",[],{"code":145,"score":52,"supported":45,"title":313,"abstract":314,"tags":315},[],[120],[120],[317,318],"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":320,"openalex_id":322,"authors":323,"venue":257,"cited_by_count":90,"oa_url":254,"card":333,"direction":338,"ingested_from":98},"W7223156204",[324,327,329,331],{"name":325,"orcid":326},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":328,"orcid":9},"Yi Zhang",{"name":330,"orcid":9},"WEI Lei",{"name":332,"orcid":9},"Chengcai Zhang",{"tldr":334,"method":335,"finding":336,"direction":95,"opportunity":337},"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":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":348,"sources":350,"tags":352,"primary_topic":357,"secondary_topics":358,"classification":359,"search_phrases":382,"slug":385,"view_count":90,"doi":386,"paper":387,"created_at":408},5490,"Biochar for post-war land remediation in Ukraine: A critical review supported by bibliometric analysis","https:\u002F\u002Fdoi.org\u002F10.1093\u002Finteam\u002Fvjag167","This critical review analyzes the current state of scientific research on biochar in Europe and examines its potential use for post-war restoration and improvement of soil fertility in Ukraine. A bibliometric analysis of scientific publications in the Scopus and Web of Science databases for the period 2014-2023 was conducted using VOSviewer visualization and data processing methods. The analysis identified the main areas of research and gaps in knowledge that are relevant to the future application of this technology in Ukraine. Moreover, remote sensing methodologies were employed to identify potential remediation requirements in the Ukrainian territory. This involved the analysis of changes in the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) of a part of Ukraine's agricultural territory situated along the frontline, prior to and following a period of three years during a full-scale war. The results revealed changes in vegetation and moisture levels, indicating land degradation in the border area under study. However, these observations do not constitute direct evidence of soil contamination or the effectiveness of biochar; rather, they indicate areas where further field studies and an assessment of restoration measures are needed. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was used as a strategic discussion tool to identify the opportunities and challenges associated with the implementation of biochar in Ukraine. This review also identifies the main scientific uncertainties, barriers, economic aspects, and research priorities that should be considered before the large-scale implementation of biochar-based technologies in Ukraine.","这篇批判性综述分析了欧洲生物炭（biochar）科学研究的现状，并探讨其在乌克兰战后恢复与土壤肥力提升中的潜在应用。研究采用VOSviewer可视化与数据处理方法，对Scopus和Web of Science数据库中2014—2023年的科学出版物进行了文献计量分析。分析识别了与该项技术在乌克兰未来应用相关的主要研究领域和知识空白。此外，研究采用遥感方法识别乌克兰境内潜在的修复需求，具体包括对乌克兰沿前线部分农业区域在全面战争前后三年期间的归一化植被指数（NDVI）和归一化水分指数（NDMI）变化进行分析。结果显示植被与水分水平发生变化，表明所研究边境地区存在土地退化。然而，这些观测结果并不构成土壤污染或生物炭有效性的直接证据，而是指示了需要进一步开展田间研究和评估修复措施的区域。研究采用优势、劣势、机会与威胁（SWOT）分析作为战略讨论工具，识别在乌克兰实施生物炭相关的机遇与挑战。本综述还明确了在乌克兰大规模实施基于生物炭的技术之前应考虑的主要科学不确定性、障碍、经济因素及研究优先事项。","Integrated Environmental Assessment and Management","2026-10-19T00:00:00Z",{"impact":179,"substance":261,"depth":19,"authority":44,"freshness":115,"relevant":21,"comment":349},"该综述结合文献计量与遥感NDVI\u002FNDMI分析，评估生物炭用于乌克兰战后农田修复的潜力与不确定性，方法新颖、结论审慎，对农业遥感与土壤治理具参考价值，但属区域性研究，未达重大突破层级。",[351],{"name":346,"url":343},[353,354,27,355,356],"生物炭","土地退化","土壤修复","乌克兰农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":360},{"candidates":361,"agricultural_evidence":45},[362,369,373,377],{"code":363,"score":44,"supported":45,"title":364,"abstract":366,"tags":368},"I7",[365],"bibliometric",[367,365],"文献计量",[],{"code":31,"score":115,"supported":45,"title":370,"abstract":371,"tags":372},[],[48,32],[355,353],{"code":63,"score":200,"supported":45,"title":374,"abstract":375,"tags":376},[],[137,138],[27],{"code":378,"score":21,"supported":14,"title":379,"abstract":380,"tags":381},"H1",[],[],[354],[383,384],"生物炭 战后土壤修复 乌克兰","NDVI NDMI 遥感 土地退化","生物炭战后土壤修复乌克兰-5490","10.1093\u002Finteam\u002Fvjag167",{"doi":386,"openalex_id":388,"authors":389,"venue":346,"cited_by_count":90,"oa_url":9,"card":402,"direction":95,"ingested_from":98},"W7223053668",[390,393,396,399],{"name":391,"orcid":392},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":394,"orcid":395},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":397,"orcid":398},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":400,"orcid":401},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":403,"method":404,"finding":405,"direction":406,"opportunity":407},"综述生物炭用于乌克兰战后土地修复，结合文献计量与遥感分析。","Scopus\u002FWoS文献计量、VOSviewer、NDVI\u002FNDMI遥感与SWO","前线农区植被与湿度变化显示土地退化，但需田间验证生物炭效果。","农业绿色发展与碳","可研究战区土地退化的遥感快速诊断与生物炭修复效果的田间验证体系。","2026-10-21T23:30:25.223030Z",{"id":410,"title":411,"url":412,"summary":413,"summary_zh":414,"content":9,"source_name":415,"source_url":412,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":416,"score_detail":417,"sources":419,"tags":421,"primary_topic":424,"secondary_topics":425,"classification":426,"search_phrases":454,"slug":457,"view_count":90,"doi":458,"paper":459,"created_at":489},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。","Discover Soil.",69,{"impact":17,"substance":18,"depth":179,"authority":114,"freshness":115,"relevant":21,"comment":418},"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[420],{"name":415,"url":412},[137,422,28,423],"土地利用","水土保持",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":427},{"candidates":428,"agricultural_evidence":45},[429,433,438,444,450],{"code":31,"score":44,"supported":45,"title":430,"abstract":431,"tags":432},[32],[48,32],[],{"code":58,"score":434,"supported":45,"title":435,"abstract":436,"tags":437},5,[],[423],[422,423],{"code":306,"score":439,"supported":45,"title":440,"abstract":441,"tags":443},3,[],[442],"水资源",[],{"code":445,"score":439,"supported":45,"title":446,"abstract":447,"tags":449},"I4",[],[448],"GIS",[],{"code":63,"score":21,"supported":14,"title":451,"abstract":452,"tags":453},[],[],[137],[455,456],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":458,"openalex_id":460,"authors":461,"venue":415,"cited_by_count":90,"oa_url":483,"card":484,"direction":95,"ingested_from":98},"W7223058039",[462,465,468,471,474,477,480],{"name":463,"orcid":464},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":466,"orcid":467},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":469,"orcid":470},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":472,"orcid":473},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":475,"orcid":476},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":478,"orcid":479},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":481,"orcid":482},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":485,"method":486,"finding":487,"direction":406,"opportunity":488},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","2026-10-21T23:30:23.420269Z",{"id":491,"title":492,"url":493,"summary":494,"summary_zh":495,"content":9,"source_name":496,"source_url":493,"published_at":176,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":497,"score_detail":498,"sources":500,"tags":502,"primary_topic":506,"secondary_topics":510,"classification":515,"search_phrases":567,"slug":570,"view_count":90,"doi":571,"paper":572,"created_at":595},5483,"Soil Salinity under Climate Change in Pakistan: Emerging Challenges and Sustainable Management Approaches for Agriculture","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.883","Soil salinity is a major constraint to agricultural productivity, soil health, and food security in Pakistan, particularly under changing climatic conditions. Rising temperatures, increased evapotranspiration, irregular precipitation, droughts, flooding, and sea-level rise can intensify salt accumulation and alter soil–water interactions. This review examines the emerging challenges associated with climate change and soil salinity in Pakistan, focusing on their impacts on soil properties, crop physiology, agricultural production, and rural livelihoods. It discusses the mechanisms of soil salinity and sodicity, their spatial distribution, and the interactions among climate variability, irrigation practices, groundwater quality, and secondary salinization. Furthermore, sustainable management approaches are evaluated, including gypsum application, organic amendments, integrated nutrient management, efficient irrigation, drainage improvement, and appropriate leaching strategies. Climate-resilient approaches, such as conventional crop breeding, modern biotechnology, halophyte utilization, plant growth-promoting microorganisms, and mycorrhizal associations, are also explored. The review highlights the potential of remote sensing, geographic information systems, precision agriculture, and predictive modelling to support salinity monitoring and site-specific management. Despite these opportunities, important challenges remain, including insufficient long-term monitoring, limited region-specific data, economic constraints, and gaps in farmer awareness and institutional coordination. Addressing these challenges requires integrated research, effective agricultural policies, improved extension services, and collaboration among researchers, policymakers, and farming communities. Prioritizing locally adapted, economically feasible, and environmentally sustainable interventions can strengthen agricultural resilience, restore salt-affected soils, and protect freshwater resources. Overall, integrating climate adaptation with sustainable soil and water management is essential for maintaining agricultural productivity and improving long-term food security in Pakistan.","土壤盐渍化是制约巴基斯坦农业生产力、土壤健康和粮食安全的主要因素，在气候变化条件下尤为突出。气温升高、蒸散加剧、降水不规律、干旱、洪水以及海平面上升可能加剧盐分积累并改变土壤—水相互作用。本文综述了巴基斯坦气候变化与土壤盐渍化相关的新兴挑战，重点关注其对土壤性质、作物生理、农业生产和农村生计的影响。文章讨论了土壤盐渍化和钠质化的机制、其空间分布，以及气候变率、灌溉实践、地下水质量和次生盐渍化之间的相互作用。此外，评估了可持续管理途径，包括施用石膏、有机改良、综合养分管理、高效灌溉、排水改良和适宜的淋洗策略。还探讨了气候韧性方法，如常规作物育种、现代生物技术、盐生植物利用、植物促生微生物和菌根共生。本文强调了遥感、地理信息系统、精准农业和预测建模在支持盐渍化监测和因地制宜管理方面的潜力。尽管存在这些机遇，重要挑战依然存在，包括长期监测不足、区域特定数据有限、经济制约以及农民意识和制度协调方面的差距。应对这些挑战需要综合研究、有效的农业政策、改进的推广服务以及研究人员、政策制定者和农业社区之间的协作。优先采取适应当地、经济可行且环境可持续的干预措施，可增强农业韧性、修复盐渍化土壤并保护淡水资源。总体而言，将气候适应与可持续水土管理相结合，对于维持巴基斯坦农业生产力和改善长期粮食安全至关重要。","Kashmir Journal of Academic Research and Development",66,{"impact":114,"substance":19,"depth":292,"authority":17,"freshness":115,"relevant":21,"comment":499},"综述性论文，系统梳理气候变化下巴基斯坦土壤盐渍化成因与可持续治理路径，兼具遥感与精准农业视角，对盐碱地治理议题有参考价值，但属区域性综述、非突破性成果。",[501],{"name":496,"url":493},[503,504,27,505,187],"气候变化","盐碱地治理","智慧灌溉",{"code":51,"slug":507,"name":508,"path":509,"parent_code":35},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[511,512],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":306,"slug":310,"name":513,"path":514,"parent_code":35},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":38,"locked":14,"version":39,"method":40,"evidence":516},{"candidates":517,"agricultural_evidence":45},[518,523,527,532,536,543,549,555,561],{"code":51,"score":19,"supported":45,"title":519,"abstract":521,"tags":522},[520],"climate change",[503,207,55,520],[503],{"code":31,"score":292,"supported":45,"title":524,"abstract":525,"tags":526},[32],[48,32],[504],{"code":306,"score":13,"supported":45,"title":528,"abstract":529,"tags":531},[],[309,442,310,530],"evapotranspiration",[505],{"code":63,"score":200,"supported":45,"title":533,"abstract":534,"tags":535},[],[137,138],[27],{"code":537,"score":101,"supported":45,"title":538,"abstract":539,"tags":542},"A8",[],[540,541],"粮食安全","food security",[],{"code":145,"score":101,"supported":45,"title":544,"abstract":545,"tags":548},[],[546,547],"精准农业","precision agriculture",[],{"code":445,"score":101,"supported":45,"title":550,"abstract":551,"tags":554},[],[552,553],"地理信息","geographic information",[],{"code":556,"score":439,"supported":45,"title":557,"abstract":558,"tags":560},"F3",[],[559],"养分管理",[],{"code":562,"score":439,"supported":45,"title":563,"abstract":564,"tags":566},"J1",[],[565],"农业政策",[],[568,569],"巴基斯坦 土壤盐渍化","盐碱地 遥感监测","巴基斯坦土壤盐渍化-5483","10.71317\u002Fkjard.2.10.2026.883",{"doi":571,"openalex_id":573,"authors":574,"venue":496,"cited_by_count":90,"oa_url":493,"card":590,"direction":95,"ingested_from":98},"W7222999158",[575,577,579,581,583,585,588],{"name":576,"orcid":9},"Asifa Naz",{"name":578,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":580,"orcid":9},"Syed Asghar Hussain Shah",{"name":582,"orcid":9},"Shahrish Nawaz",{"name":584,"orcid":9},"Muhmmad Irfan",{"name":586,"orcid":587},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":589,"orcid":9},"Muhammad Nouman Khalid",{"tldr":591,"method":592,"finding":593,"direction":95,"opportunity":594},"综述气候变化下巴基斯坦土壤盐渍化的挑战与可持续管理途径。","文献综述，整合盐渍化机制、空间分布及遥感GIS与预测模型应用。","需将气候适应与水土管理结合，优先本地化、经济可行的干预措施。","可填补长期盐渍化监测与区域数据空白，发展遥感+预测模型的精准分区治理。","2026-10-21T23:30:23.059448Z"]