[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2675":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":62},2675,"Long-term ecological quality dynamics and spatial associations in arid and semi-arid Northwest China using an adaptive PRSEI","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71853-z","Long-term ecological monitoring in arid and semi-arid regions requires indices that capture region-specific stressors while remaining comparable through time. We developed an adaptive particular remote-sensing ecological index (PRSEI) for Gansu Province, China, using growing-season kNDVI, wetness (WET), land surface temperature (LST), sandification index (SI), and a PM10-based particulate indicator (TI) from 2000 to 2024. A unified principal component analysis (PCA) with common scaling defined the primary time series, while annual PCA and fixed weights were used for sensitivity testing. Unified PC1 explained 79.12% of total variance, and mean PRSEI increased from 0.343 to 0.419 (slope = 0.00432 yr⁻¹; R² = 0.778). Unified PCA and fixed weights agreed on 99.90% of pixel-level trend directions, whereas annual and unified PCA agreed on only 39.73%, indicating strong methodological sensitivity. Adaptive PRSEI was highly correlated with original PRSEI ( r = 0.991) and traditional RSEI ( r = 0.975), showing the strongest consistency with mapped restoration transitions (81.47%) but weaker consistency with degradation transitions (34.22%). Precipitation, soil type, and land use\u002Fland cover were the most stable spatial explanatory factors. Overall, ecological quality improved broadly but heterogeneously, supporting adaptive PRSEI as a complementary multi-stressor index for drylands rather than a universal replacement for RSEI.","干旱与半干旱地区的长期生态监测需要既能反映区域特有胁迫因子、又能在时间上保持可比性的指数。本研究针对中国甘肃省构建了一种自适应特定遥感生态指数（PRSEI），所用数据为2000—2024年生长季的kNDVI、湿度（WET）、地表温度（LST）、沙化指数（SI）以及基于PM10的颗粒物指标（TI）。采用统一主成分分析（PCA）与共同标准化方案确定主时间序列，同时以逐年PCA和固定权重进行敏感性检验。统一PC1解释了总方差的79.12%，PRSEI均值从0.343上升至0.419（斜率=0.00432 yr⁻¹；R²=0.778）。统一PCA与固定权重在99.90%的像元水平趋势方向上一致，而逐年PCA与统一PCA仅在一致性为39.73%，表明方法敏感性较强。自适应PRSEI与原始PRSEI高度相关（r=0.991），与传统RSEI也高度相关（r=0.975），其与制图恢复转变的一致性最高（81.47%），但与退化转变的一致性较弱（34.22%）。降水、土壤类型和土地利用\u002F土地覆盖是最稳定的空间解释因子。总体而言，生态质量广泛改善但存在空间异质性，支持将自适应PRSEI作为干旱区多胁迫因子的补充性指数，而非RSEI的通用替代方案。",null,"Scientific Reports","2026-09-15T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"提出自适应遥感生态指数PRSEI并揭示西北旱区生态质量长期改善趋势，方法新颖、数据扎实，对旱区农业生态监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业信息化","甘肃","遥感监测","生态质量","干旱半干旱区",0,"10.1038\u002Fs41598-026-71853-z",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":54,"card":55,"direction":59,"ingested_from":61},"W7213360699",[37,39,41,43,45,47,50,52],{"name":38,"orcid":9},"Jiangmin Wu",{"name":40,"orcid":9},"Bin Lian",{"name":42,"orcid":9},"Qirui Zhang",{"name":44,"orcid":9},"Zhen Yan",{"name":46,"orcid":9},"Wei Zhao",{"name":48,"orcid":49},"Jiachen Yang","https:\u002F\u002Forcid.org\u002F0000-0003-2558-552X",{"name":51,"orcid":9},"Zaixing Chen",{"name":53,"orcid":9},"Yuxiang Lan","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71853-z_reference.pdf",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"构建自适应PRSEI指数，评估2000-2024年甘肃生态质量动态与空间关联。","基于生长季kNDVI、湿度、地表温度、沙化指数和PM10指标，用统一PCA构建P","生态质量整体改善但异质，PRSEI与RSEI高度一致，方法敏感性显著。","农业遥感与作物表型","可探索多胁迫指数在干旱区不同尺度的适用性，并耦合人类活动与政策因素。","openalex","2026-09-16T23:30:30.948770Z"]