[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5490":3,"related-5490":101},{"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":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":100},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。",null,"Integrated Environmental Assessment and Management","2026-10-19T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"生物炭","土地退化","遥感监测","土壤修复","乌克兰农业",{"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},[45,53,58,66],{"code":46,"score":20,"supported":47,"title":48,"abstract":50,"tags":52},"I7",true,[49],"bibliometric",[51,49],"文献计量",[],{"code":33,"score":21,"supported":47,"title":54,"abstract":55,"tags":57},[],[56,34],"土壤",[30,27],{"code":59,"score":60,"supported":47,"title":61,"abstract":62,"tags":65},"I3",7,[],[63,64],"遥感","remote sensing",[29],{"code":67,"score":22,"supported":14,"title":68,"abstract":69,"tags":70},"H1",[],[],[28],[72,73],"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",0,"10.1093\u002Finteam\u002Fvjag167",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":75,"oa_url":9,"card":92,"direction":98,"ingested_from":99},"W7223053668",[80,83,86,89],{"name":81,"orcid":82},"Olha Shomko","https:\u002F\u002Forcid.org\u002F0000-0003-2418-0985",{"name":84,"orcid":85},"Iryna Davydova","https:\u002F\u002Forcid.org\u002F0000-0001-6535-3948",{"name":87,"orcid":88},"Marco Bartoli","https:\u002F\u002Forcid.org\u002F0000-0002-5022-6206",{"name":90,"orcid":91},"Elena Maestri","https:\u002F\u002Forcid.org\u002F0000-0002-8745-9564",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"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。","农业遥感与作物表型","openalex","2026-10-21T23:30:25.223030Z",{"total":102,"page":22,"page_size":102,"items":103},6,[104,208,289,358,432,508],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":117,"tags":119,"primary_topic":123,"secondary_topics":124,"classification":125,"search_phrases":190,"slug":193,"view_count":167,"doi":194,"paper":195,"created_at":207},3770,"Biochar in Ecological Restoration of Degraded Lands, Soil Health, and Food Crops Safety","https:\u002F\u002Fdoi.org\u002F10.1002\u002Ftqem.70468","ABSTRACT Agro‐chemicals intensive agriculture, coupled with biomass burning in ‘ethno‐agricultural systems’ or shifting cultivated Jhum lands, stress the global agroecosystems with soil degradation and expanding marginal lands, causing food security and public health safety challenges. The pressing limitations of physico‐chemical treatment methods, underscore the quest for designer or engineered biochar. However, abundantly generated agro‐wastes utilization in ‘Agricultural‐residues‐derived‐Biochar (ARBC)’ production are inadequately explored for eco‐restoring soil health. Deploying ARBCs may offer land restoration by addressing heavy metals\u002Fmetalloids pollution and revitalizing soil fertility. Present review, therefore envisions to fill the existing knowledge voids in ARBC‐mediated ameliorations of soil quality, crops yield, and food safety, using standard methodologies. Results reveal that agro‐waste biochar leverages soil restoration by modulating soil‐physico‐chemical characteristics, microbial enzymes, and oxidative stress tolerance mechanisms. Further, techno‐economic advances in manoeuvring nano‐biochar can augment the ARBC‐efficiencies as ‘slow release nutrient fertilizers’, C‐sequestration bioagent, metals bio‐adsorbent, together resulting in ‘climate change‐resilient‐smart agriculture’. Moreover, ARBC‐amended ‘agroecosystem engineering’ for land eco‐restoration are aligned with the accomplishment of several ‘Sustainable Development Goals’ (SDGs). Nevertheless, existing limitations in terms of application protocols, optimizing biochar‐dose, standardized feedstock selections, pollutant's release on biochar aging, together constraints long‐term scalable ARBC‐applications. In conclusion, targeted future researches, devising standardized methods to investigate the ‘Life Cycle Assessment’ and possible ecological risks of repeated long‐term scalable biochar use can address these challenges. Prioritizing the field‐scale studies on commercial ARBC‐applications, in‐tuned with techno‐economic, scientific, nanotechnology, biotechnological, and risk‐assessment advances can potentially restore the degraded lands to help achieve sustainability paradigm in agriculture systems.","摘要：农用化学品的集约化农业，加之“民族农业系统”或轮作制刀耕火种（Jhum）土地中的生物质燃烧，使全球农业生态系统承受着土壤退化与边缘土地扩张的压力，进而引发粮食安全和公共卫生安全方面的挑战。物理化学处理方法的紧迫局限性，凸显了对定制或工程化生物炭（biochar）的探索需求。然而，在“农业废弃物衍生生物炭（Agricultural-residues-derived-Biochar, ARBC）”生产中，大量产生的农业废弃物的利用在生态修复土壤健康方面尚缺乏充分研究。应用ARBC可通过治理重金属\u002F类金属污染和恢复土壤肥力来实现土地修复。因此，本综述旨在运用标准方法学，填补ARBC介导的土壤质量改良、作物产量提升及食品安全保障方面现有的知识空白。结果表明，农业废弃物生物炭通过调控土壤理化特性、微生物酶及氧化胁迫耐受机制来促进土壤修复。此外，操控纳米生物炭的技术经济进展可增强ARBC作为“缓释养分肥料”、碳封存生物制剂、金属生物吸附剂的效能，共同促成“气候变化韧性智慧农业”。而且，ARBC改良的“农业生态系统工程”用于土地生态修复，与多项“可持续发展目标”（SDGs）的实现相契合。然而，在应用方案、生物炭剂量优化、标准化原料选择、生物炭老化过程中污染物释放等方面存在的局限性，共同制约了ARBC应用的长期可扩展性。总之，有针对性的未来研究，设计标准化方法以考察“生命周期评估”及反复长期可扩展使用生物炭可能带来的生态风险，可应对这些挑战。优先开展商业ARBC应用的田间尺度研究，并与技术经济、科学、纳米技术、生物技术及风险评估进展相协调，有望修复退化土地，助力实现农业系统的可持续性范式。","Environmental Quality Management","2026-09-28T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":114,"freshness":115,"relevant":22,"comment":116},13,9,"系统综述农业废弃物生物炭在退化土地修复、土壤健康与作物安全中的作用，方法学与结论具参考价值，但属综述类论文，产业落地影响有限。",[118],{"name":110,"url":107},[120,27,121,122,30],"粮食安全","农业废弃物","固碳减排",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":126},{"candidates":127,"agricultural_evidence":47},[128,132,138,145,152,159,165,172,178,184],{"code":33,"score":17,"supported":47,"title":129,"abstract":130,"tags":131},[34],[56,34],[30,27],{"code":133,"score":60,"supported":47,"title":134,"abstract":135,"tags":137},"A8",[],[120,136],"food security",[120],{"code":139,"score":102,"supported":47,"title":140,"abstract":141,"tags":144},"E4",[],[142,143],"食品安全","food safety",[],{"code":146,"score":102,"supported":47,"title":147,"abstract":148,"tags":151},"H7",[],[149,150],"气候变化","climate change",[],{"code":153,"score":102,"supported":47,"title":154,"abstract":155,"tags":158},"I1",[],[156,157],"智慧农业","smart agriculture",[],{"code":160,"score":161,"supported":47,"title":162,"abstract":163,"tags":164},"H5",4,[],[121],[121],{"code":166,"score":167,"supported":47,"title":168,"abstract":169,"tags":171},"A7",3,[],[170],"轮作",[],{"code":173,"score":167,"supported":47,"title":174,"abstract":175,"tags":177},"F3",[],[176],"肥料",[],{"code":179,"score":167,"supported":47,"title":180,"abstract":181,"tags":183},"H6",[],[182],"农业生态",[],{"code":185,"score":167,"supported":47,"title":186,"abstract":187,"tags":189},"J3",[],[188],"全球农业",[],[191,192],"农业废弃物 生物炭 土壤修复","生物炭 退化土地 粮食安全","农业废弃物生物炭土壤修复-3770","10.1002\u002Ftqem.70468",{"doi":194,"openalex_id":196,"authors":197,"venue":110,"cited_by_count":75,"oa_url":9,"card":201,"direction":206,"ingested_from":99},"W7214589462",[198],{"name":199,"orcid":200},"Prabhat Kumar","https:\u002F\u002Forcid.org\u002F0000-0001-5373-4321",{"tldr":202,"method":203,"finding":204,"direction":96,"opportunity":205},"综述农业废弃物生物炭修复退化土壤、提升作物产量与食品安全的研究进展与挑战。","综述农业废弃物生物炭（ARBC）及纳米生物炭在土壤修复中的应用与标准化方法。","ARBC可改善土壤理化性质、微生物酶活与氧化应激耐受，但施用规范与长期风险仍待解决。","可开展田间尺度ARBC长期施用生命周期评估与生态风险研究，并制定标准化施用方案。","智慧农业 \u002F 农业物联网","2026-09-29T23:30:09.071026Z",{"id":209,"title":210,"url":211,"summary":212,"summary_zh":213,"content":9,"source_name":214,"source_url":211,"published_at":215,"category":12,"cover_url":9,"hotness":216,"is_selected":14,"score":217,"score_detail":218,"sources":222,"tags":226,"primary_topic":230,"secondary_topics":236,"classification":242,"search_phrases":269,"slug":272,"view_count":161,"doi":273,"paper":274,"created_at":288},3279,"ASSESSMENT OF SOIL SALINIZATION IN IRRIGATED LANDS BASED ON REMOTE SENSING AND GIS TECHNOLOGIES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900049","This thesis analyzes modern approaches to monitoring and assessing soil salinity in irrigated areas of Uzbekistan and Central Asia. Soil salinization is one of the major land degradation processes affecting the reclamation condition of irrigated lands, crop growth and agricultural productivity. Although conventional field and laboratory surveys provide detailed information, they have limitations in terms of regular monitoring of large areas. Therefore, remote sensing and geographic information systems provide promising tools for rapid and spatial assessment of soil salinity. The study considers the application of satellite imagery, particularly Sentinel-2 and Landsat data, for soil salinity assessment, including the use of spectral bands and salinity indices, GIS-based thematic mapping, and validation using field and laboratory observations. Recent studies demonstrate the potential of remote sensing data for spatial assessment and mapping of soil salinity under the conditions of Uzbekistan. Such an integrated approach can support rapid monitoring of the reclamation status of irrigated lands, identification of salt-affected areas and spatial planning of appropriate reclamation measures.","本论文分析了乌兹别克斯坦和中亚灌溉区土壤盐分监测与评估的现代方法。土壤盐渍化是影响灌溉土地改良状况、作物生长和农业生产力的主要土地退化过程之一。尽管传统的野外调查和实验室分析能够提供详细信息，但在大范围定期监测方面存在局限性。因此，遥感与地理信息系统为土壤盐分的快速空间评估提供了有前景的工具。本研究探讨了卫星影像，特别是Sentinel-2和Landsat数据在土壤盐分评估中的应用，包括光谱波段和盐分指数的使用、基于GIS的主题制图，以及利用野外和实验室观测数据进行验证。近期研究表明，遥感数据在乌兹别克斯坦条件下用于土壤盐分空间评估与制图具有潜力。这种综合方法可支持灌溉土地改良状况的快速监测、盐渍化区域的识别以及适当改良措施的空间规划。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,66,{"impact":219,"substance":19,"depth":220,"authority":219,"freshness":115,"relevant":22,"comment":221},12,15,"基于遥感与GIS的灌溉区土壤盐渍化监测论文，方法成熟、结论可靠，对盐碱地治理与农业信息化有参考价值，但属区域性研究，影响层级有限。",[223,224],{"name":214,"url":211},{"name":214,"url":225},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22900048",[28,29,227,228,229],"GIS","土壤盐渍化","灌溉农业",{"code":231,"slug":232,"name":233,"path":234,"parent_code":235},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[237,241],{"code":59,"slug":238,"name":239,"path":240,"parent_code":235},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":243},{"candidates":244,"agricultural_evidence":47},[245,251,255,259,265],{"code":231,"score":19,"supported":47,"title":246,"abstract":247,"tags":250},[227],[248,227,249],"地理信息","geographic information",[227],{"code":59,"score":220,"supported":47,"title":252,"abstract":253,"tags":254},[64],[63,64],[29],{"code":33,"score":20,"supported":47,"title":256,"abstract":257,"tags":258},[34],[56,34],[],{"code":260,"score":161,"supported":47,"title":261,"abstract":262,"tags":264},"H3",[],[263],"灌溉",[229],{"code":67,"score":22,"supported":14,"title":266,"abstract":267,"tags":268},[],[],[28],[270,271],"乌兹别克斯坦 土壤盐渍化 遥感","Sentinel-2 盐渍化 灌溉","乌兹别克斯坦土壤盐渍化遥感-3279","10.5281\u002Fzenodo.22900049",{"doi":273,"openalex_id":275,"authors":276,"venue":214,"cited_by_count":75,"oa_url":211,"card":283,"direction":98,"ingested_from":99},"W7214052773",[277,279,281],{"name":278,"orcid":9},"Mardiyev",{"name":280,"orcid":9},"Ergashaliyev",{"name":282,"orcid":9},"G'aniyev",{"tldr":284,"method":285,"finding":286,"direction":98,"opportunity":287},"综述遥感与GIS评估乌兹别克斯坦及中亚灌溉土壤盐渍化的方法。","Sentinel-2与Landsat影像、光谱盐分指数、GIS制图及野外验证。","遥感与GIS可快速空间评估盐渍化，支持灌溉地改良状态监测与规划。","可探索多源遥感与机器学习融合的盐分指数模型，提升干旱区盐渍化反演精度与时效。","2026-09-23T23:30:19.341486Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":295,"source_url":292,"published_at":296,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":297,"score_detail":298,"sources":302,"tags":304,"primary_topic":308,"secondary_topics":309,"classification":316,"search_phrases":338,"slug":341,"view_count":102,"doi":342,"paper":343,"created_at":357},3174,"Remote sensing and geospatial modeling for the detection of war-induced land use abandonment, soil disturbance, and degradation in Sumy region of Ukraine: a systematic review","https:\u002F\u002Fdoi.org\u002F10.31548\u002Fzemleustriy2026.03.015","The full-scale military invasion of Ukraine has caused multidimensional transformations of agricultural landscapes, land cover, and ecosystems. The Sumy region, located along the northeastern border with the Russian Federation, is a critical region that has suffered from intense fighting, prolonged artillery shelling, and ongoing border clashes during the study period from 2022 to May 2026. This paper systematically evaluates the application of remote sensing and geospatial modeling techniques to detect, quantify, and monitor war-induced land abandonment, as well as physical land cover disturbances, chemical contamination, and broader environmental degradation in this theater of war. Combining recent scientific publications on the war in Ukraine and comparative global examples (Syria, Iraq, and Sudan), the effectiveness of integrating multi-sensor data, including optical (Sentinel-2, Landsat), synthetic aperture radar (SAR; Sentinel-1), thermal, and very high resolution (VHR; Maxar, WorldView) platforms, as well as advanced machine and deep learning algorithms (Random Forest, Deep Learning) and time series analysis (FANTA, two-period curve approximation), is assessed. The results of the study show that while geospatial modeling is an indispensable tool for rapid, large-scale, and non-contact damage assessment in active conflict settings, significant critical research gaps remain, namely separating short-term cessation of agricultural use from actual land abandonment caused by war, verifying such facts in the face of a severe shortage of reliable ground data, and managing sensitive geospatial information. This systematic review highlights existing methodological gaps and outlines future perspectives needed to build a harmonized spatial monitoring system capable of guiding post-conflict recovery and environmental remediation based on actual remote sensing data at the regional scale.Received: 01.07.2026;Accepted:26.08.2026;","乌克兰遭受的大规模军事入侵已导致农业景观、土地覆盖和生态系统发生多维转变。苏梅州位于乌克兰与俄罗斯联邦东北部接壤的边境沿线，是一个关键地区，在研究期间（2022年至2026年5月）遭受了激烈战斗、长期炮击以及持续边境冲突的影响。本文系统评估了遥感与地理空间建模技术在探测、量化和监测该战区战争导致的土地撂荒、土地覆盖物理扰动、化学污染及更广泛环境退化方面的应用。结合近期关于乌克兰战争的科学出版物及全球比较案例（叙利亚、伊拉克和苏丹），本文评估了多传感器数据整合的有效性，包括光学（Sentinel-2、Landsat）、合成孔径雷达（SAR；Sentinel-1）、热红外及甚高分辨率（VHR；Maxar、WorldView）平台，以及先进的机器学习和深度学习算法（随机森林、深度学习）和时间序列分析（FANTA、两期曲线近似）。研究结果表明，尽管地理空间建模是在活跃冲突环境中进行快速、大规模、非接触式损害评估不可或缺的工具，但仍存在重要的关键研究空白，即如何区分农业利用的短期中止与战争导致的实际土地撂荒，如何在严重缺乏可靠地面数据的情况下核实此类事实，以及如何管理敏感的地理空间信息。本系统综述指出了现有的方法论空白，并勾勒了未来前景，以构建一个协调的空间监测系统，从而基于区域尺度上的实际遥感数据指导冲突后恢复和环境修复。收稿日期：2026年7月1日；接受日期：2026年8月26日；","Zemleustrìj kadastr ì monìtorìng zemelʹ","2026-09-21T00:00:00Z",75,{"impact":19,"substance":299,"depth":300,"authority":219,"freshness":21,"relevant":22,"comment":301},20,17,"系统综述遥感与地理空间建模在战区耕地撂荒与土壤退化识别中的应用，方法体系与数据源梳理扎实，对农业遥感监测有参考价值，但属境外冲突场景研究，国内落地关联度有限。",[303],{"name":295,"url":292},[305,28,29,306,307],"农业人工智能","耕地撂荒","冲突农业",{"code":59,"slug":238,"name":239,"path":240,"parent_code":235},[310,315],{"code":311,"slug":312,"name":313,"path":314,"parent_code":235},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":317},{"candidates":318,"agricultural_evidence":47},[319,323,330,334],{"code":59,"score":220,"supported":47,"title":320,"abstract":321,"tags":322},[64],[63,64],[29],{"code":311,"score":13,"supported":47,"title":324,"abstract":325,"tags":329},[],[326,327,328],"深度学习","机器学习","deep learning",[305],{"code":33,"score":21,"supported":47,"title":331,"abstract":332,"tags":333},[34],[],[],{"code":67,"score":22,"supported":14,"title":335,"abstract":336,"tags":337},[],[],[28],[339,340],"乌克兰 苏梅州 遥感 耕地撂荒","Sentinel-1 合成孔径雷达 土地退化监测","乌克兰苏梅州遥感耕地撂荒-3174","10.31548\u002Fzemleustriy2026.03.015",{"doi":342,"openalex_id":344,"authors":345,"venue":295,"cited_by_count":75,"oa_url":292,"card":352,"direction":98,"ingested_from":99},"W7213890485",[346,348,350],{"name":347,"orcid":9},"V. Bogdanets",{"name":349,"orcid":9},"Ye. Berezhniak",{"name":351,"orcid":9},"D. Brovko",{"tldr":353,"method":354,"finding":355,"direction":98,"opportunity":356},"系统综述遥感与地理空间建模在乌克兰苏梅地区检测战争导致的土地弃耕、土壤扰动与退化的应用。","综述多源遥感（Sentinel-2\u002F1、Landsat、VHR）与机器学习、时间","遥感是冲突区快速非接触损害评估的关键工具，但区分短期停耕与真正弃耕、地面数据匮乏及敏感信息管理仍是重","可研究多源时序遥感与弱监督学习结合，构建冲突区弃耕识别与地面验证缺失下的不确定性量化框架。","2026-09-22T23:30:23.289241Z",{"id":359,"title":360,"url":361,"summary":362,"summary_zh":363,"content":9,"source_name":364,"source_url":361,"published_at":365,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":366,"sources":369,"tags":371,"primary_topic":374,"secondary_topics":375,"classification":376,"search_phrases":401,"slug":404,"view_count":22,"doi":405,"paper":406,"created_at":431},2791,"Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101524","Land degradation remains a major environmental challenge, particularly in semi-arid and heterogeneous landscapes, where interactions between vegetation loss and soil exposure are complex and spatially dynamic. This study, therefore, seeks to evaluate the spatial extent of land degradation and drivers over time (2005 - 2025) using Landsat data series and support vector machine (SVM) in the Keiskamma Catchment of South Africa. Degraded land followed a non-monotonic trajectory: it declined from ∼197 km 2 in 2005 to ∼157 km 2 in 2015 (a temporary contraction of 20.3%, consistent with short-term restoration and land-use shifts), before rising sharply and unsustainably to ∼328 km 2 by 2025 (a 108.9% increase relative to 2015, and a net increase of 66.5% over the full two-decade period), largely at the expense of grassland and agricultural land. Furthermore, the findings show that soil-sensitive indicators, particularly BSI and SWIR spectral bands, play a crucial role in determining degraded land. In contrast, vegetation indices such as NDVI contribute less under degraded conditions because degraded areas were severely dominated by exposed soil rather than vegetation. Correlation matrix analysis further reveals a temporal shift from mixed soil–vegetation spectral relationships toward strong soil-dominated reflectance patterns by 2025, indicating advanced degradation stages. Overall, the integration of SVM classification with VIF and SHAP provides a transparent, reliable, and spatially explicit framework for monitoring land degradation. The findings support land degradation neutrality monitoring and provide critical insights for sustainable land-management planning in support of Sustainable Development Goal (SDG) 15.3.","土地退化仍然是一项重大环境挑战，尤其是在半干旱和异质性景观中，植被丧失与土壤裸露之间的相互作用复杂且具有空间动态性。因此，本研究旨在利用Landsat数据序列和支持向量机（SVM），评估南非Keiskamma集水区2005—2025年间土地退化的空间范围及其驱动因素。退化土地呈非单调变化轨迹：从2005年的约197 km²下降至2015年的约157 km²（暂时收缩20.3%，与短期恢复和土地利用变化相一致），随后急剧且不可持续地上升至2025年的约328 km²（较2015年增加108.9%，在整个二十年期间净增加66.5%），且主要以草地和农用地为代价。此外，研究结果表明，土壤敏感指标，尤其是BSI和SWIR光谱波段，在判定退化土地方面发挥着关键作用。相比之下，NDVI等植被指数在退化条件下贡献较小，因为退化区域严重以裸露土壤为主，而非植被。相关矩阵分析进一步揭示，到2025年，光谱关系由土壤—植被混合关系向强烈的土壤主导反射模式发生时间转变，表明退化已进入后期阶段。总体而言，将SVM分类与VIF和SHAP相结合，为监测土地退化提供了一个透明、可靠且具有空间显式性的框架。研究结果支持土地退化零增长监测，并为支持可持续发展目标（SDG）15.3的可持续土地管理规划提供了关键见解。","Environmental and Sustainability Indicators","2026-09-16T00:00:00Z",{"impact":220,"substance":367,"depth":19,"authority":114,"freshness":115,"relevant":22,"comment":368},22,"基于Landsat时序与SVM\u002FSHAP的南非土地退化监测研究，方法透明可复现，对农业遥感与土地退化中性监测有参考价值，但属区域案例、非国内三农直接政策信息。",[370],{"name":364,"url":361},[372,327,373,28,29],"农业遥感","可持续发展",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":377},{"candidates":378,"agricultural_evidence":47},[379,383,388,393,397],{"code":33,"score":102,"supported":47,"title":380,"abstract":381,"tags":382},[],[56,34],[],{"code":146,"score":167,"supported":47,"title":384,"abstract":385,"tags":387},[],[386],"干旱",[],{"code":59,"score":389,"supported":14,"title":390,"abstract":391,"tags":392},2,[],[],[372,29],{"code":67,"score":22,"supported":14,"title":394,"abstract":395,"tags":396},[],[],[28],{"code":311,"score":22,"supported":14,"title":398,"abstract":399,"tags":400},[],[],[327],[402,403],"可持续发展 农业遥感 土地退化 机器学习","可持续发展 农业遥感","可持续发展农业遥感土地退化机器学习-2791","10.1016\u002Fj.indic.2026.101524",{"doi":405,"openalex_id":407,"authors":408,"venue":364,"cited_by_count":75,"oa_url":425,"card":426,"direction":98,"ingested_from":99},"W7213298151",[409,411,414,417,419,422],{"name":410,"orcid":9},"Mandisa Zameko",{"name":412,"orcid":413},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":415,"orcid":416},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":418,"orcid":9},"Matthieu Tshanga",{"name":420,"orcid":421},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":423,"orcid":424},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":427,"method":428,"finding":429,"direction":98,"opportunity":430},"用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。","Landsat时序数据、SVM分类，结合VIF与SHAP做特征解释。","退化面积先降后升，2025年达328km²，土壤光谱指标比NDVI更关键。","可将该SVM-SHAP框架迁移到其他半干旱区，并耦合气候与土地利用驱动做退化预警。","2026-09-17T23:30:36.054539Z",{"id":433,"title":434,"url":435,"summary":436,"summary_zh":437,"content":9,"source_name":438,"source_url":435,"published_at":439,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":440,"sources":442,"tags":444,"primary_topic":448,"secondary_topics":449,"classification":450,"search_phrases":470,"slug":473,"view_count":75,"doi":474,"paper":475,"created_at":507},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",{"impact":17,"substance":367,"depth":19,"authority":20,"freshness":115,"relevant":22,"comment":441},"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。",[443],{"name":438,"url":435},[29,445,228,446,447],"莫桑比克","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":451},{"candidates":452,"agricultural_evidence":47},[453,457,461,466],{"code":33,"score":20,"supported":47,"title":454,"abstract":455,"tags":456},[34],[56,34],[],{"code":59,"score":60,"supported":47,"title":458,"abstract":459,"tags":460},[],[63,64],[29],{"code":146,"score":102,"supported":47,"title":462,"abstract":463,"tags":465},[],[464,386],"气候适应",[],{"code":179,"score":22,"supported":14,"title":467,"abstract":468,"tags":469},[],[],[446],[471,472],"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":474,"openalex_id":476,"authors":477,"venue":438,"cited_by_count":75,"oa_url":435,"card":502,"direction":98,"ingested_from":99},"W7223069171",[478,480,482,484,487,489,492,495,497,499],{"name":479,"orcid":9},"Yue Li",{"name":481,"orcid":9},"Mengfei Shi",{"name":483,"orcid":9},"Fei Zhang",{"name":485,"orcid":486},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":488,"orcid":9},"Gowhar Meraj",{"name":490,"orcid":491},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":493,"orcid":494},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":496,"orcid":9},"Yuanfang Chai",{"name":498,"orcid":9},"Hongtao Xu",{"name":500,"orcid":501},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":503,"method":504,"finding":505,"direction":98,"opportunity":506},"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":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":9,"source_name":514,"source_url":511,"published_at":515,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":516,"sources":518,"tags":520,"primary_topic":523,"secondary_topics":529,"classification":532,"search_phrases":564,"slug":567,"view_count":75,"doi":568,"paper":569,"created_at":586},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",{"impact":17,"substance":18,"depth":300,"authority":114,"freshness":13,"relevant":22,"comment":517},"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。",[519],{"name":514,"url":511},[156,327,29,521,522],"土壤墒情","冬小麦",{"code":524,"slug":525,"name":526,"path":527,"parent_code":528},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[530,531],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":311,"slug":312,"name":313,"path":314,"parent_code":235},{"status":40,"locked":14,"version":41,"method":42,"evidence":533},{"candidates":534,"agricultural_evidence":47},[535,541,545,550,555,560],{"code":524,"score":536,"supported":47,"title":537,"abstract":539,"tags":540},26,[538,525],"winter wheat",[526,538,525],[522],{"code":33,"score":220,"supported":47,"title":542,"abstract":543,"tags":544},[34],[56,34],[521],{"code":311,"score":220,"supported":47,"title":546,"abstract":548,"tags":549},[547],"machine learning",[327,547],[327],{"code":59,"score":13,"supported":47,"title":551,"abstract":552,"tags":554},[],[63,64,553],"multispectral",[29],{"code":260,"score":102,"supported":47,"title":556,"abstract":557,"tags":559},[],[263,558],"irrigation",[],{"code":153,"score":161,"supported":47,"title":561,"abstract":562,"tags":563},[],[156],[156],[565,566],"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":568,"openalex_id":570,"authors":571,"venue":514,"cited_by_count":75,"oa_url":511,"card":581,"direction":206,"ingested_from":99},"W7223156204",[572,575,577,579],{"name":573,"orcid":574},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":576,"orcid":9},"Yi Zhang",{"name":578,"orcid":9},"WEI Lei",{"name":580,"orcid":9},"Chengcai Zhang",{"tldr":582,"method":583,"finding":584,"direction":98,"opportunity":585},"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。","2026-10-22T11:20:18.887301Z"]