[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-SIF":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"SIF","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":29,"tags":31,"search_phrases":36,"slug":39,"view_count":40,"doi":41,"paper":42,"created_at":66},3674,"Mapping and dynamic monitoring of desertification on the Qinghai-Tibetan Plateau using surface Albedo and Solar-Induced Chlorophyll Fluorescence","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0359348","Desertification poses a significant threat to humanity's long-term survival and sustainable development. In this study, four types of 2D desertification assessment models were created by integrating surface albedo with four vegetation-related indicators: NDVI, MSAVI, EVI, and solar-induced chlorophyll fluorescence (SIF). A comprehensive analysis of the spatiotemporal variations in desertification on the Qinghai-Tibetan Plateau (QTP) from 2001 to 2020 was then conducted. Among the four models, the SA-SIF model demonstrated the highest overall accuracy (0.8675; 95% CI: 0.8512-0.8838) and Kappa coefficient (0.8452; 95% CI: 0.8289-0.8615), significantly outperforming the other three models (p \u003C 0.01, McNemar's test). The SA-SIF model's mapping results revealed substantial regional variability in desertification on the QTP, with the degree of desertification decreasing from northwest to southeast. Over the past decade, the expansion of severe and extremely severe desertification on the QTP has shown signs of slowing, although future trends may become more complex due to the interplay of climatic and anthropogenic factors. The SA-SIF model can serve as a reference for future desertification control operations on the QTP.","荒漠化对人类长期生存与可持续发展构成重大威胁。本研究通过将地表反照率与四种植被相关指标——归一化植被指数（NDVI）、修正土壤调整植被指数（MSAVI）、增强型植被指数（EVI）和日光诱导叶绿素荧光（SIF）——相结合，构建了四类二维荒漠化评估模型，并据此对2001年至2020年青藏高原荒漠化的时空变化进行了综合分析。在四种模型中，SA-SIF模型的总体精度（0.8675；95%置信区间：0.8512–0.8838）和Kappa系数（0.8452；95%置信区间：0.8289–0.8615）最高，显著优于其他三种模型（p \u003C 0.01，McNemar检验）。SA-SIF模型的制图结果揭示了青藏高原荒漠化存在显著的区域差异，荒漠化程度自西北向东南递减。过去十年间，青藏高原重度及极重度荒漠化的扩张呈现放缓迹象，但受气候与人为因素交互作用的影响，未来趋势可能趋于复杂。SA-SIF模型可为青藏高原未来的荒漠化治理工作提供参考。",null,"PLoS ONE","2026-09-25T00:00:00Z","论文",10,false,78,{"impact":24,"substance":25,"depth":24,"authority":26,"freshness":27,"relevant":7,"comment":28},18,22,14,6,"提出SA-SIF荒漠化评估模型并揭示青藏高原2001-2020年荒漠化时空演变，方法新颖、结论可靠，对高原生态治理有参考价值。",[30],{"name":17,"url":13},[32,33,34,4,35],"荒漠化","遥感监测","青藏高原","生态治理",[37,38],"青藏高原 荒漠化 遥感监测","SA-SIF 模型 叶绿素荧光","青藏高原荒漠化遥感监测-3674",0,"10.1371\u002Fjournal.pone.0359348",{"doi":41,"openalex_id":43,"authors":44,"venue":17,"cited_by_count":40,"oa_url":58,"card":59,"direction":63,"ingested_from":65},"W7214339598",[45,48,51,53,55],{"name":46,"orcid":47},"Zhijian Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4764-2943",{"name":49,"orcid":50},"Hui Lin","https:\u002F\u002Forcid.org\u002F0000-0003-1278-4351",{"name":52,"orcid":16},"Lei Wu",{"name":54,"orcid":16},"Linling Tang",{"name":56,"orcid":57},"Xin Xiao","https:\u002F\u002Forcid.org\u002F0000-0001-8859-7240","https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pone.0359348&type=printable",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"构建地表反照率与SIF结合的荒漠化评估模型，监测青藏高原2001-2020年荒漠化动态。","融合地表反照率与NDVI、MSAVI、EVI、SIF构建四种二维模型，对比精度。","SA-SIF模型精度最高（总体精度0.8675），荒漠化程度自西北向东南递减，重度扩张减缓。","农业遥感与作物表型","可探索SIF与多源遥感协同的荒漠化早期预警，并量化气候与人类活动贡献。","openalex","2026-09-28T23:30:30.884081Z"]