[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4233":3,"related-4233":95},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":94},4233,"IMPACT OF ANTHROPOGENIC ACTIVITIES (AGRICULTURE, MINING, URBANIZATION) ON VEGETATION AND SOILS IN MARITIME GUINEA (2002–2024)","https:\u002F\u002Fdoi.org\u002F10.30574\u002Fwjarr.2026.32.1.2501","The sub-prefecture of Damakhania, in Maritime Guinea, is undergoing rapid landscape transformation driven by the The landscapes of the Damakhania sub-prefecture, in Maritime Guinea, are being rapidly reshaped by the combined effects of urban expansion, agriculture and climate variability. This study examines changes in vegetation and hydroclimatic conditions between 1986 and 2024, quantifies land-use\u002Fland-cover (LULC) change between 2002 and 2024, and explores possible trajectories up to 2100. Fifteen variables were analysed: seven Landsat-derived indices (NDVI, EVI, SAVI, MSAVI, NBR, NDWI and LST) and eight climatic and water-balance variables. Trends and change points were assessed with the Mann-Kendall, Sen's slope and Pettitt tests, and seasonal contrasts with Student's t-test and the Mann-Whitney test. LULC maps for 2002 (Landsat 5 TM) and 2024 (Landsat 8 OLI) were used to measure change and to build three prospective scenarios. EVI declined significantly (τ = −0.385; p \u003C 0.001), whereas land surface temperature rose by about 0.23 °C per year (τ = 0.397; p \u003C 0.001). The Pettitt test indicated significant inhomogeneity in the EVI, NBR and LST series, while precipitation, maximum air temperature and NDVI showed no significant trend. Over 22 years, wooded\u002Fshrub savanna and open forest lost 47.4% and 45.7% of their area, respectively, to the benefit of urban fabric (+203.4%), degraded areas and bare soil (+189.7%) and crops and fallow land (+33.5%). The scenarios suggest that, without conservation measures, natural vegetation could disappear before 2050. The observed degradation appears more closely related to human pressure than to an unfavourable rainfall trend, which calls for land-use planning that protects the remaining wooded areas.","几内亚沿海地区的达马哈尼亚（Damakhania）副省，其景观正因城市扩张、农业活动和气候变率的综合影响而迅速改变。本研究考察了1986年至2024年间植被与水文气候条件的变化，量化了2002年至2024年间的土地利用\u002F土地覆盖（LULC）变化，并探讨了至2100年的可能演变轨迹。共分析了15个变量：7个基于Landsat的指数（NDVI、EVI、SAVI、MSAVI、NBR、NDWI和LST）以及8个气候与水量平衡变量。趋势与突变点分别采用Mann-Kendall检验、Sen斜率估计和Pettitt检验进行评估，季节性差异则采用Student's t检验和Mann-Whitney检验。利用2002年（Landsat 5 TM）和2024年（Landsat 8 OLI）的LULC图件测量变化并构建了三种情景预测。EVI显著下降（τ = −0.385；p \u003C 0.001），而地表温度以约每年0.23 °C的速率上升（τ = 0.397；p \u003C 0.001）。Pettitt检验表明EVI、NBR和LST序列存在显著的非均一性，而降水、最高气温和NDVI未显示出显著趋势。22年间，疏林\u002F灌丛草原和疏林分别丧失了47.4%和45.7%的面积，而城市建成区（+203.4%）、退化区域和裸地（+189.7%）以及农田和休耕地（+33.5%）则相应增加。情景预测表明，若不采取保护措施，自然植被可能在2050年前消失。观测到的退化似乎与人类压力关系更为密切，而非不利的降水趋势所致，这要求通过土地利用规划来保护残余的林地。",null,"World Journal of Advanced Research and Reviews","2026-10-03T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,22,17,12,9,1,"基于Landsat多指数与LULC情景模拟的实证研究，数据扎实、结论明确，但属区域案例，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","植被退化","土地利用变化","荒漠化防治","国土空间规划",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,51,57],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":50},3,true,[],[34],[],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"H6",2,[],[],[29,30],{"code":58,"score":53,"supported":14,"title":59,"abstract":60,"tags":61},"I3",[],[],[28,27],[63,64],"Damakhania 土地利用 遥感","Maritime Guinea 植被退化","Damakhania土地利用遥感-4233",0,"10.30574\u002Fwjarr.2026.32.1.2501",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":66,"oa_url":86,"card":87,"direction":91,"ingested_from":93},"W7218954171",[71,73,75,77,80,82,84],{"name":72,"orcid":9},"Alpha Laho Diallo",{"name":74,"orcid":9},"Kadiatou Diallo",{"name":76,"orcid":9},"Thierno Boubacar BAH",{"name":78,"orcid":79},"Mamadou Yaya Baldé","https:\u002F\u002Forcid.org\u002F0000-0002-6439-4702",{"name":81,"orcid":9},"Sory Keita",{"name":83,"orcid":9},"Alpha Oumar",{"name":85,"orcid":9},"DIALLO Alpha Issaga Pallé DIALLO","https:\u002F\u002Fwjarr.com\u002Fsites\u002Fdefault\u002Ffiles\u002Ffulltext_pdf\u002FWJARR-2026-2501.pdf",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"分析几内亚达马卡尼亚2002–2024年农业、采矿与城市化对植被和土壤的影响并预测至2100年。","Landsat指数与气候变量结合Mann-Kendall、Sen斜率、Petti","EVI显著下降、地表温度年增约0.23°C，林灌与疏林22年减少约46%，退化主因是人为压力而非降水","农业遥感与作物表型","可结合高分辨率遥感与农户调查，量化采矿、城市扩张对植被退化的相对贡献并评估保护情景成效。","openalex","2026-10-05T23:30:25.259465Z",{"total":96,"page":22,"page_size":96,"items":97},6,[98,185,280,349,457,525],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":106,"score_detail":107,"sources":112,"tags":114,"primary_topic":119,"secondary_topics":120,"classification":121,"search_phrases":147,"slug":150,"view_count":66,"doi":151,"paper":152,"created_at":184},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":108,"substance":18,"depth":109,"authority":110,"freshness":21,"relevant":22,"comment":111},16,18,14,"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。",[113],{"name":104,"url":101},[27,115,116,117,118],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":122},{"candidates":123,"agricultural_evidence":47},[124,129,136,143],{"code":33,"score":110,"supported":47,"title":125,"abstract":126,"tags":128},[34],[127,34],"土壤",[],{"code":58,"score":130,"supported":47,"title":131,"abstract":132,"tags":135},7,[],[133,134],"遥感","remote sensing",[27],{"code":137,"score":96,"supported":47,"title":138,"abstract":139,"tags":142},"H7",[],[140,141],"气候适应","干旱",[],{"code":52,"score":22,"supported":14,"title":144,"abstract":145,"tags":146},[],[],[117],[148,149],"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":151,"openalex_id":153,"authors":154,"venue":104,"cited_by_count":66,"oa_url":101,"card":179,"direction":91,"ingested_from":93},"W7223069171",[155,157,159,161,164,166,169,172,174,176],{"name":156,"orcid":9},"Yue Li",{"name":158,"orcid":9},"Mengfei Shi",{"name":160,"orcid":9},"Fei Zhang",{"name":162,"orcid":163},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":165,"orcid":9},"Gowhar Meraj",{"name":167,"orcid":168},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":170,"orcid":171},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":173,"orcid":9},"Yuanfang Chai",{"name":175,"orcid":9},"Hongtao Xu",{"name":177,"orcid":178},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":180,"method":181,"finding":182,"direction":91,"opportunity":183},"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":186,"title":187,"url":188,"summary":189,"summary_zh":190,"content":9,"source_name":191,"source_url":188,"published_at":192,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":194,"sources":198,"tags":200,"primary_topic":205,"secondary_topics":211,"classification":219,"search_phrases":256,"slug":259,"view_count":66,"doi":260,"paper":261,"created_at":279},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":108,"substance":195,"depth":19,"authority":196,"freshness":13,"relevant":22,"comment":197},21,13,"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。",[199],{"name":191,"url":188},[201,202,27,203,204],"智慧农业","机器学习","土壤墒情","冬小麦",{"code":206,"slug":207,"name":208,"path":209,"parent_code":210},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[212,213],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":214,"slug":215,"name":216,"path":217,"parent_code":218},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":220},{"candidates":221,"agricultural_evidence":47},[222,228,233,238,243,250],{"code":206,"score":223,"supported":47,"title":224,"abstract":226,"tags":227},26,[225,207],"winter wheat",[208,225,207],[204],{"code":33,"score":229,"supported":47,"title":230,"abstract":231,"tags":232},15,[34],[127,34],[203],{"code":214,"score":229,"supported":47,"title":234,"abstract":236,"tags":237},[235],"machine learning",[202,235],[202],{"code":58,"score":13,"supported":47,"title":239,"abstract":240,"tags":242},[],[133,134,241],"multispectral",[27],{"code":244,"score":96,"supported":47,"title":245,"abstract":246,"tags":249},"H3",[],[247,248],"灌溉","irrigation",[],{"code":251,"score":252,"supported":47,"title":253,"abstract":254,"tags":255},"I1",4,[],[201],[201],[257,258],"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":260,"openalex_id":262,"authors":263,"venue":191,"cited_by_count":66,"oa_url":188,"card":273,"direction":278,"ingested_from":93},"W7223156204",[264,267,269,271],{"name":265,"orcid":266},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":268,"orcid":9},"Yi Zhang",{"name":270,"orcid":9},"WEI Lei",{"name":272,"orcid":9},"Chengcai Zhang",{"tldr":274,"method":275,"finding":276,"direction":91,"opportunity":277},"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":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":193,"score_detail":288,"sources":290,"tags":292,"primary_topic":297,"secondary_topics":298,"classification":299,"search_phrases":322,"slug":325,"view_count":66,"doi":326,"paper":327,"created_at":348},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":108,"substance":195,"depth":109,"authority":110,"freshness":17,"relevant":22,"comment":289},"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。",[291],{"name":286,"url":283},[293,294,27,295,296],"生物炭","土地退化","土壤修复","乌克兰农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":300},{"candidates":301,"agricultural_evidence":47},[302,309,313,317],{"code":303,"score":110,"supported":47,"title":304,"abstract":306,"tags":308},"I7",[305],"bibliometric",[307,305],"文献计量",[],{"code":33,"score":17,"supported":47,"title":310,"abstract":311,"tags":312},[],[127,34],[295,293],{"code":58,"score":130,"supported":47,"title":314,"abstract":315,"tags":316},[],[133,134],[27],{"code":318,"score":22,"supported":14,"title":319,"abstract":320,"tags":321},"H1",[],[],[294],[323,324],"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":326,"openalex_id":328,"authors":329,"venue":286,"cited_by_count":66,"oa_url":9,"card":342,"direction":91,"ingested_from":93},"W7223053668",[330,333,336,339],{"name":331,"orcid":332},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":334,"orcid":335},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":337,"orcid":338},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":340,"orcid":341},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":343,"method":344,"finding":345,"direction":346,"opportunity":347},"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":350,"title":351,"url":352,"summary":353,"summary_zh":354,"content":9,"source_name":355,"source_url":352,"published_at":105,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":357,"sources":359,"tags":361,"primary_topic":365,"secondary_topics":369,"classification":374,"search_phrases":428,"slug":431,"view_count":66,"doi":432,"paper":433,"created_at":456},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":196,"substance":109,"depth":229,"authority":20,"freshness":17,"relevant":22,"comment":358},"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。",[360],{"name":355,"url":352},[362,363,27,364,116],"气候变化","盐碱地治理","智慧灌溉",{"code":137,"slug":366,"name":367,"path":368,"parent_code":37},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",[370,371],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":244,"slug":248,"name":372,"path":373,"parent_code":37},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":40,"locked":14,"version":41,"method":42,"evidence":375},{"candidates":376,"agricultural_evidence":47},[377,382,386,392,396,403,409,416,422],{"code":137,"score":109,"supported":47,"title":378,"abstract":380,"tags":381},[379],"climate change",[362,140,141,379],[362],{"code":33,"score":229,"supported":47,"title":383,"abstract":384,"tags":385},[34],[127,34],[363],{"code":244,"score":13,"supported":47,"title":387,"abstract":388,"tags":391},[],[247,389,248,390],"水资源","evapotranspiration",[364],{"code":58,"score":130,"supported":47,"title":393,"abstract":394,"tags":395},[],[133,134],[27],{"code":397,"score":96,"supported":47,"title":398,"abstract":399,"tags":402},"A8",[],[400,401],"粮食安全","food security",[],{"code":251,"score":96,"supported":47,"title":404,"abstract":405,"tags":408},[],[406,407],"精准农业","precision agriculture",[],{"code":410,"score":96,"supported":47,"title":411,"abstract":412,"tags":415},"I4",[],[413,414],"地理信息","geographic information",[],{"code":417,"score":46,"supported":47,"title":418,"abstract":419,"tags":421},"F3",[],[420],"养分管理",[],{"code":423,"score":46,"supported":47,"title":424,"abstract":425,"tags":427},"J1",[],[426],"农业政策",[],[429,430],"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":432,"openalex_id":434,"authors":435,"venue":355,"cited_by_count":66,"oa_url":352,"card":451,"direction":91,"ingested_from":93},"W7222999158",[436,438,440,442,444,446,449],{"name":437,"orcid":9},"Asifa Naz",{"name":439,"orcid":9},"Muhammad Abu Baker Siddiq",{"name":441,"orcid":9},"Syed Asghar Hussain Shah",{"name":443,"orcid":9},"Shahrish Nawaz",{"name":445,"orcid":9},"Muhmmad Irfan",{"name":447,"orcid":448},"Rizwana Kausar","https:\u002F\u002Forcid.org\u002F0000-0002-1339-3312",{"name":450,"orcid":9},"Muhammad Nouman Khalid",{"tldr":452,"method":453,"finding":454,"direction":91,"opportunity":455},"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",{"id":458,"title":459,"url":460,"summary":461,"summary_zh":462,"content":9,"source_name":463,"source_url":460,"published_at":464,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":465,"score_detail":466,"sources":468,"tags":470,"primary_topic":475,"secondary_topics":476,"classification":477,"search_phrases":506,"slug":509,"view_count":22,"doi":510,"paper":511,"created_at":524},4506,"Spatial assessment of soil degradation under intensive agricultural pressure in the central loess plateau","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1940953","Soil degradation (SD) threatens the sustainability of semi-arid agricultural systems in the Central Loess Plateau, where intensive cultivation and repeated land-use cycles accelerate soil deterioration. This study developed a spatially explicit and uncertainty-aware framework for assessing and mapping SD by integrating soil quality indicators (SQIs), environmental data, remote sensing, and Random Forest (RF) modelling. A total of 350 surface-soil samples were collected and analyzed. Principal component analysis and the minimum data set (MDS) approach were used to identify representative soil-quality indicators. The final MDS included clay, soil bulk density, pH, soil organic carbon, calcium carbonate equivalent, cation exchange capacity, potassium, and sodium adsorption ratio. Among these indicators, pH, SAR, CEC, SOC, K, and clay received the greatest weights, highlighting the importance of soil chemical conditions, sodicity, nutrient retention, organic matter, and texture in soil quality assessment. Soil degradation was quantified by comparing SQI values of agricultural soils with reference values from natural ecosystems. Environmental predictors were then incorporated into an RF model to estimate the spatial distribution of SD and assess predictor importance. The model showed good predictive performance, with R 2 values of 0.83 for training and 0.76 for validation, and RMSE values ranging from 0.031 to 0.034. Severe degradation was mainly concentrated in the central and southwestern regions, reflecting the cumulative effects of intensive cultivation, nutrient depletion, and SOC loss. Prediction-interval analysis identified areas with greater uncertainty and priority for additional sampling. The proposed framework provides a practical basis for spatially targeted soil conservation, sustainable agricultural management, and long-term ecosystem resilience. Future research should incorporate biological indicators and temporal monitoring to improve SD prediction under changing climatic and land-use conditions.","土壤退化（SD）威胁着黄土高原中部半干旱农业系统的可持续性，该地区集约化耕作和反复的土地利用周期加速了土壤恶化。本研究通过整合土壤质量指标（SQIs）、环境数据、遥感技术和随机森林（RF）建模，开发了一个空间明确且具有不确定性感知的土壤退化评估与制图框架。共采集并分析了350个表层土壤样品。采用主成分分析和最小数据集（MDS）方法识别代表性土壤质量指标。最终的最小数据集包括黏粒、土壤容重、pH、土壤有机碳、碳酸钙当量、阳离子交换量、钾和钠吸附比。在这些指标中，pH、SAR、CEC、SOC、K和黏粒获得了最大的权重，凸显了土壤化学条件、钠质化、养分保持、有机质和质地在土壤质量评估中的重要性。通过比较农业土壤的SQI值与自然生态系统的参考值，对土壤退化进行了量化。随后将环境预测因子纳入随机森林模型，以估算土壤退化的空间分布并评估预测因子的重要性。该模型表现出良好的预测性能，训练集R²值为0.83，验证集R²值为0.76，RMSE值在0.031至0.034之间。严重退化主要集中在中部和西南部地区，反映了集约化耕作、养分耗竭和有机碳损失的累积效应。预测区间分析识别了不确定性较大且需要优先补充采样的区域。所提出的框架为空间针对性的土壤保护、可持续农业管理和长期生态系统恢复力提供了实践基础。未来研究应纳入生物指标和时间监测，以改进气候变化和土地利用变化条件下的土壤退化预测。","Frontiers in Environmental Science","2026-10-08T00:00:00Z",76,{"impact":229,"substance":18,"depth":109,"authority":196,"freshness":17,"relevant":22,"comment":467},"基于350个样点与随机森林模型的空间化土壤退化评估，方法扎实、结论可靠，对黄土高原耕地保护有参考价值，但属区域学术研究，公共影响有限。",[469],{"name":463,"url":460},[471,472,27,473,474],"随机森林","耕地保护","土壤退化","黄土高原",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":478},{"candidates":479,"agricultural_evidence":47},[480,484,488,492,498,502],{"code":33,"score":110,"supported":47,"title":481,"abstract":482,"tags":483},[34],[127,34],[],{"code":58,"score":130,"supported":47,"title":485,"abstract":486,"tags":487},[],[133,134],[27],{"code":137,"score":96,"supported":47,"title":489,"abstract":490,"tags":491},[],[362,141],[],{"code":493,"score":46,"supported":47,"title":494,"abstract":495,"tags":497},"H8",[],[496],"可持续农业",[],{"code":318,"score":22,"supported":14,"title":499,"abstract":500,"tags":501},[],[],[472],{"code":214,"score":22,"supported":14,"title":503,"abstract":504,"tags":505},[],[],[471],[507,508],"黄土高原 土壤退化 遥感","土壤质量 随机森林 评估","黄土高原土壤退化遥感-4506","10.3389\u002Ffenvs.2026.1940953",{"doi":510,"openalex_id":512,"authors":513,"venue":463,"cited_by_count":66,"oa_url":460,"card":519,"direction":91,"ingested_from":93},"W7221006056",[514,516],{"name":515,"orcid":9},"Xiandong Hou",{"name":517,"orcid":518},"Tao Bai","https:\u002F\u002Forcid.org\u002F0000-0002-2034-8026",{"tldr":520,"method":521,"finding":522,"direction":91,"opportunity":523},"构建融合土壤指标、遥感与随机森林的空间显式框架，评估黄土高原中部高强度农业下的土壤退化。","350个表层土样，PCA与最小数据集筛选指标，随机森林建模并做预测区间分析。","pH、SAR、CEC、SOC、钾和黏粒权重最高；严重退化集中于中部和西南部。","可引入生物指标与多时相遥感监测，提升气候变化和土地利用变化下土壤退化预测的时空精度。","2026-10-09T23:30:25.996370Z",{"id":526,"title":527,"url":528,"summary":529,"summary_zh":530,"content":9,"source_name":531,"source_url":528,"published_at":532,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":533,"sources":535,"tags":537,"primary_topic":541,"secondary_topics":542,"classification":544,"search_phrases":586,"slug":589,"view_count":22,"doi":590,"paper":591,"created_at":621},4367,"Pasture ecosystems of the arid zone of southern Kazakhstan: current state, problems and ways to restoration (review)","https:\u002F\u002Fdoi.org\u002F10.51886\u002F1999-740x_2026_3_107","The article presents an overview of modern studies of pasture ecosystems in the arid zone of Southern Kazakhstan using the example of the Otyrar district of the Turkestan region. The article examines the region's natural and climatic features, and the influence of aridization and climate change on the structure of pasture phytocenoses, their productivity, and biodiversity. The main factors of sustainability of pasture ecosystems were analyzed, including water regime, soil salinity, organic carbon content, the state of the soil microbiome, and anthropogenic load. The purpose of this review article is to analyze and summarize recent scientific research on the state, functioning, assessment methods, and prospects for the restoration of pasture ecosystems in the arid zone of Southern Kazakhstan, using the example of the Otyrar district of the Turkestan region. Particular attention is paid to modern methods of geobotanical assessment of vegetation, determination of projective cover, productivity, and biodiversity indicators, as well as to the use of geographic information systems, remote sensing, and satellite monitoring to assess the condition of pastures. The data on the prospects for the restoration of degraded pastures through the use of organomineral fertilizers based on natural aluminosilicates, humic substances, protein hydrolysate, and microalgae Chlorella vulgaris are summarized. It has been shown that an integrated approach combining geobotanical, soil-ecological, and digital research methods contributes to increasing the sustainability of pasture ecosystems, preserving biodiversity, and rational use of natural forage resources in arid territories.","本文以突厥斯坦州奥特尔区为例，综述了南哈萨克斯坦干旱区牧场生态系统的现代研究进展。文章考察了该地区的自然与气候特征，以及干旱化和气候变化对牧场植物群落结构、生产力和生物多样性的影响。分析了牧场生态系统可持续性的主要因素，包括水分状况、土壤盐渍化、有机碳含量、土壤微生物组状态和人为负荷。这篇综述性文章旨在以突厥斯坦州奥特尔区为例，分析和总结近年来关于南哈萨克斯坦干旱区牧场生态系统状态、功能、评估方法及恢复前景的科学研究。特别关注植被地植物学评估、投影盖度测定、生产力和生物多样性指标的现代方法，以及地理信息系统、遥感和卫星监测在牧场状况评估中的应用。综述了利用基于天然铝硅酸盐、腐殖物质、蛋白质水解物和微藻普通小球藻（Chlorella vulgaris）的有机矿物肥料恢复退化牧场的前景数据。研究表明，结合地植物学、土壤生态学和数字研究方法的综合途径有助于提高干旱地区牧场生态系统的可持续性、保护生物多样性并合理利用天然饲料资源。","Pochvovedenie i agrokhimiya","2026-10-05T00:00:00Z",{"impact":20,"substance":109,"depth":108,"authority":20,"freshness":17,"relevant":22,"comment":534},"综述性论文，系统梳理干旱区牧场生态评估与恢复技术，方法学与遥感应用有参考价值，但属区域研究，公共影响有限。",[536],{"name":531,"url":528},[363,27,538,539,540],"草原生态","土壤微生物","退化草地修复",{"code":137,"slug":366,"name":367,"path":368,"parent_code":37},[543],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":545},{"candidates":546,"agricultural_evidence":47},[547,551,555,561,565,572,576,581],{"code":137,"score":21,"supported":47,"title":548,"abstract":549,"tags":550},[],[362,141,379],[],{"code":33,"score":17,"supported":47,"title":552,"abstract":553,"tags":554},[],[127,34],[539,363],{"code":52,"score":130,"supported":47,"title":556,"abstract":557,"tags":560},[],[558,559],"生物多样性","biodiversity",[538],{"code":58,"score":130,"supported":47,"title":562,"abstract":563,"tags":564},[],[133,134],[27],{"code":566,"score":96,"supported":47,"title":567,"abstract":568,"tags":571},"C4",[],[569,570],"饲料","forage",[],{"code":410,"score":96,"supported":47,"title":573,"abstract":574,"tags":575},[],[413,414],[],{"code":417,"score":46,"supported":47,"title":577,"abstract":578,"tags":580},[],[579],"肥料",[],{"code":303,"score":46,"supported":47,"title":582,"abstract":583,"tags":585},[],[584],"研究方法",[],[587,588],"哈萨克斯坦 南部 牧场 生态","Otyrar 退化草地 恢复","哈萨克斯坦南部牧场生态-4367","10.51886\u002F1999-740x_2026_3_107",{"doi":590,"openalex_id":592,"authors":593,"venue":531,"cited_by_count":66,"oa_url":615,"card":616,"direction":91,"ingested_from":93},"W7220716738",[594,597,600,603,606,609,612],{"name":595,"orcid":596},"Assem Mukangalyieva","https:\u002F\u002Forcid.org\u002F0009-0003-2507-4428",{"name":598,"orcid":599},"Arailym Amanzholkyzy","https:\u002F\u002Forcid.org\u002F0000-0003-4052-1436",{"name":601,"orcid":602},"Galymzhan A. Saparov","https:\u002F\u002Forcid.org\u002F0000-0002-6392-2032",{"name":604,"orcid":605},"Talgat Ibragimov","https:\u002F\u002Forcid.org\u002F0000-0001-8902-9042",{"name":607,"orcid":608},"Jiefei Mao","https:\u002F\u002Forcid.org\u002F0000-0002-5225-1747",{"name":610,"orcid":611},"A.D Gazizov","https:\u002F\u002Forcid.org\u002F0009-0006-8763-5632",{"name":613,"orcid":614},"A. S. Sakhbek","https:\u002F\u002Forcid.org\u002F0009-0003-3670-8828","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1140\u002F807",{"tldr":617,"method":618,"finding":619,"direction":91,"opportunity":620},"综述哈萨克斯坦南部干旱区牧场生态系统现状、问题及恢复途径。","文献综述，结合地植物学评估、GIS、遥感和卫星监测。","综合地植物学、土壤生态与数字方法可提升牧场可持续性并保护生物多样性。","可探索遥感与土壤微生物、有机碳结合的干旱牧场退化早期预警与精准恢复模型。","2026-10-07T23:30:30.164155Z"]