[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3074":3,"related-3074":112},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":79,"slug":82,"view_count":73,"doi":83,"paper":84,"created_at":111},3074,"Temporal Distribution of Drought Indices Using Remote Sensing in the Desertification Nuclei of Brazil","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i03.269019","Desertification is one of the main environmental threats to the Brazilian Semi-Arid region, causing biodiversity loss, soil degradation, and a decline in agricultural productivity. Monitoring droughts through remote sensing is fundamental to understanding climate dynamics and supporting mitigation strategies. In this respect, the objective of this study was to analyze the temporal variability of drought in four recognized desertification nuclei in Northeast Brazil: Cabrobó, Gilbués, Irauçuba, and Seridó, from 2001 to 2024. The methodology consisted of evaluating drought scenarios using the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI), derived from the MODIS sensor. The results indicated a strong seasonality, with more critical values ​​in the second half of the year, when VCI, TCI, and VHI frequently fell below 30, characterizing moderate to severe drought. The core of the Seridó region presented the highest levels of vulnerability, with average VCI and VHI values ​​below 40 and a strong correlation with precipitation (r > 0.60). The Mann-Kendall test did not indicate statistically significant monthly trends, suggesting stability in the historical drought pattern, although regression analysis revealed signs of worsening in Gilbués. Thus, the effectiveness of SR-derived indices in characterizing drought events and highlighting the combined influence of climatic, edaphic, and anthropogenic factors was confirmed. It is concluded that systematic monitoring using SR is essential to support adaptation and mitigation policies in the face of desertification in the Brazilian Semi-Arid region.","荒漠化是巴西半干旱地区面临的主要环境威胁之一，会导致生物多样性丧失、土壤退化以及农业生产力下降。通过遥感监测干旱是理解气候动态并支撑缓解策略的基础。在此背景下，本研究旨在分析2001年至2024年间巴西东北部四个公认的荒漠化核心区——卡布罗博、吉尔布埃斯、伊拉乌苏巴和塞里多——干旱的时间变异性。研究方法包括利用源自MODIS传感器的植被状态指数（VCI）、温度状态指数（TCI）和植被健康指数（VHI）评估干旱情景。结果表明存在强烈的季节性，下半年数值更为严峻，VCI、TCI和VHI频繁低于30，表现为中度至重度干旱。塞里多核心区脆弱性最高，VCI和VHI平均值低于40，且与降水量呈强相关（r > 0.60）。Mann-Kendall检验未显示统计学显著的月度趋势，表明历史干旱模式具有稳定性，尽管回归分析揭示了吉尔布埃斯存在恶化的迹象。因此，遥感衍生指数在刻画干旱事件以及揭示气候、土壤和人为因素综合影响方面的有效性得到证实。结论是，系统性的遥感监测对于支持巴西半干旱地区应对荒漠化的适应与缓解政策至关重要。",null,"Journal of Hyperspectral Remote Sensing","2026-09-19T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"基于MODIS遥感指数分析巴西四大荒漠化核心区2001—2024年干旱时序变化，方法规范、数据跨度长，对遥感干旱监测有参考价值，但属区域性研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"荒漠化","遥感监测","干旱监测","巴西半干旱区","植被健康指数",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42,"reason":78},[39,48,55,63,71],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"H7",15,true,[44],"drought",[46,44],"干旱",[29],{"code":49,"score":41,"supported":42,"title":50,"abstract":52,"tags":54},"I3",[51],"remote sensing",[53,51],"遥感",[28],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":62},"H6",7,[],[60,61],"生物多样性","biodiversity",[27],{"code":64,"score":65,"supported":42,"title":66,"abstract":67,"tags":70},"H2",6,[],[68,69],"土壤","soil",[],{"code":72,"score":73,"supported":42,"title":74,"abstract":75,"tags":77},"I7",3,[],[76],"研究方法",[],"多个产业证据相当，需要确认主归属",[80,81],"巴西 荒漠化 遥感 干旱","巴西半干旱区 植被健康指数 干旱监测 遥感监测","巴西荒漠化遥感干旱-3074","10.29150\u002Fjhrs.v16i03.269019",{"doi":83,"openalex_id":85,"authors":86,"venue":10,"cited_by_count":103,"oa_url":6,"card":104,"direction":108,"ingested_from":110},"W7213779605",[87,90,92,95,97,100],{"name":88,"orcid":89},"Juarez Antônio da Silva Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":91,"orcid":9},"Gabriel Henrique Santos Silva",{"name":93,"orcid":94},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":96,"orcid":9},"Sylvana Sylvana Melo dos Santos",{"name":98,"orcid":99},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":101,"orcid":102},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",0,{"tldr":105,"method":106,"finding":107,"direction":108,"opportunity":109},"基于MODIS遥感指数分析巴西四个荒漠化核心区2001-2024年干旱时空变化。","用MODIS数据计算VCI、TCI、VHI，结合Mann-Kendall趋势检验","干旱呈强季节性，下半年最严重；Seridó最脆弱，Gilbués有恶化迹象。","农业遥感与作物表型","可结合多源遥感与机器学习构建荒漠化区干旱预警模型，并量化人为因素贡献。","openalex","2026-09-21T23:30:25.707774Z",{"total":65,"page":22,"page_size":65,"items":113},[114,177,245,317,381,441],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"content":9,"source_name":120,"source_url":117,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":126,"tags":128,"primary_topic":9,"secondary_topics":132,"classification":133,"search_phrases":155,"slug":158,"view_count":103,"doi":159,"paper":160,"created_at":176},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi","2026-10-19T00:00:00Z",61,{"impact":21,"substance":124,"depth":41,"authority":17,"freshness":21,"relevant":22,"comment":125},18,"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[127],{"name":120,"url":117},[129,28,29,130,131],"NDVI","NDMI","LST",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":134},{"candidates":135,"agricultural_evidence":14,"reason":154},[136,140,144,148],{"code":40,"score":41,"supported":42,"title":137,"abstract":138,"tags":139},[44],[46,44],[29],{"code":49,"score":21,"supported":42,"title":141,"abstract":142,"tags":143},[],[53,51],[129,28],{"code":64,"score":65,"supported":42,"title":145,"abstract":146,"tags":147},[],[68,69],[],{"code":149,"score":73,"supported":42,"title":150,"abstract":151,"tags":153},"H3",[],[152],"水资源",[],"农业相关性需要复核",[156,157],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":159,"openalex_id":161,"authors":162,"venue":120,"cited_by_count":103,"oa_url":169,"card":170,"direction":175,"ingested_from":110},"W7222954725",[163,166],{"name":164,"orcid":165},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":167,"orcid":168},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":171,"method":172,"finding":173,"direction":108,"opportunity":174},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":191,"tags":193,"primary_topic":9,"secondary_topics":196,"classification":197,"search_phrases":213,"slug":216,"view_count":22,"doi":217,"paper":218,"created_at":244},5177,"Long-term associations between land-surface indicators and dust-storm occurrence along the margins of the Taklimakan Desert","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75251-3","Land-surface change and atmospheric variability jointly shape dust activity in arid regions. We examined land-cover transitions, station dust-storm records, a Sentinel-1 backscatter index, meteorological variables from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and dust diagnostics from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) along the margins of the Taklimakan Desert. Within the mapped domain, grassland increased from 9.45% in 1990 to 10.59% in 2023, while barren land decreased from 86.45% to 82.30%. Annual dust-storm-day trends were negative at 21 of 30 stations over available 1990–2024 records, and Sentinel-1 index trends were positive at 22 stations during 2015–2024. Mixed-effects path analysis of 3,804 station-month observations showed that the Sentinel-1 index was negatively associated with modelled dust emission (standardized coefficient = − 0.191; 95% confidence interval: −0.289 to − 0.043). The normalized difference vegetation index (NDVI) and friction velocity had the highest training-set feature importance in the random forest analysis.","地表变化与大气变率共同塑造了干旱地区的沙尘活动。我们沿塔克拉玛干沙漠边缘，研究了土地覆盖转变、台站沙尘暴记录、Sentinel-1后向散射指数、来自欧洲中期天气预报中心第五代再分析资料（ERA5）的气象变量，以及来自现代研究与应用回顾分析第2版（MERRA-2）的沙尘诊断量。在制图范围内，草地面积从1990年的9.45%增加到2023年的10.59%，而裸地面积从86.45%减少到82.30%。在1990—2024年现有记录中，30个台站中有21个台站的年沙尘暴日数呈下降趋势；2015—2024年间，22个台站的Sentinel-1指数呈上升趋势。对3804个台站—月观测值的混合效应路径分析表明，Sentinel-1指数与模拟沙尘排放呈负相关（标准化系数=−0.191；95%置信区间：−0.289至−0.043）。在随机森林分析中，归一化植被指数（NDVI）和摩擦速度在训练集中的特征重要性最高。","Scientific Reports","2026-10-15T00:00:00Z",78,{"impact":187,"substance":188,"depth":124,"authority":189,"freshness":21,"relevant":22,"comment":190},16,22,14,"基于多源遥感与30站长期记录，量化地表指标与沙尘暴关联，方法扎实、数据规模可观，对干旱区生态监测有参考价值。",[192],{"name":183,"url":180},[129,27,28,194,195],"土地利用","沙尘暴",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":198},{"candidates":199,"agricultural_evidence":14,"reason":154},[200,204,209],{"code":40,"score":73,"supported":42,"title":201,"abstract":202,"tags":203},[],[46],[],{"code":56,"score":205,"supported":14,"title":206,"abstract":207,"tags":208},2,[],[],[194,27],{"code":49,"score":205,"supported":14,"title":210,"abstract":211,"tags":212},[],[],[129,28],[214,215],"塔克拉玛干沙漠 沙尘暴 遥感","Sentinel-1 土地覆盖 沙尘","塔克拉玛干沙漠沙尘暴遥感-5177","10.1038\u002Fs41598-026-75251-3",{"doi":217,"openalex_id":219,"authors":220,"venue":183,"cited_by_count":103,"oa_url":238,"card":239,"direction":108,"ingested_from":110},"W7222086957",[221,223,226,229,231,233,235],{"name":222,"orcid":9},"Yifei Wang",{"name":224,"orcid":225},"Wen Huo","https:\u002F\u002Forcid.org\u002F0000-0002-7861-2424",{"name":227,"orcid":228},"Mayibaier Maihamuti","https:\u002F\u002Forcid.org\u002F0009-0007-4084-5891",{"name":230,"orcid":9},"Fan Yang",{"name":232,"orcid":9},"Chenglong Zhou",{"name":234,"orcid":9},"Xinghua Yang",{"name":236,"orcid":237},"Ali Mamtimin","https:\u002F\u002Forcid.org\u002F0000-0001-7335-801X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75251-3_reference.pdf",{"tldr":240,"method":241,"finding":242,"direction":108,"opportunity":243},"分析塔克拉玛干沙漠边缘地表指标与沙尘暴发生的长期关联。","结合土地覆盖、站点沙尘记录、Sentinel-1后向散射、ERA5与MERRA-","站点沙尘日数多呈下降趋势，Sentinel-1指数与模拟沙尘排放显著负相关。","可引入多源遥感与机器学习量化地表变化对沙尘的因果贡献，并拓展至绿洲农业防护。","2026-10-17T23:30:30.235613Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":9,"source_name":251,"source_url":248,"published_at":252,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":253,"score_detail":254,"sources":257,"tags":259,"primary_topic":9,"secondary_topics":263,"classification":264,"search_phrases":291,"slug":294,"view_count":205,"doi":295,"paper":296,"created_at":316},5099,"Geospatial analysis of land degradation and desertification in the semi-arid Shabelle River Basin, Somalia","https:\u002F\u002Fdoi.org\u002F10.1080\u002F10106049.2026.2746835","Land degradation and desertification pose major environmental challenges in semi-arid regions such as the Shabelle River Basin in Somalia. This study assessed vulnerability to land degradation and desertification from 2000 to 2025 using multi-temporal Landsat images, CHIRPS rainfall data, Google Earth Engine and GIS techniques. A Desertification Vulnerability Index (DVI) was developed by combining nine environmental indicators, including NDVI, SAVI, BSI, SI, NDBI, NDMI, MNDWI, LST and rainfall, using a weighted overlay analysis. Results indicate that large parts of the basin are vulnerable, with the Shabelle River corridor and southern regions comparatively less affected, while other areas show declines in vegetation and environmental health. The DVI shows a temporal trend toward higher vulnerability. It has been effectively used to assess spatial desertification vulnerability and validated with 341 reference points, showing good discrimination for 2000, 2015 and 2025.","土地退化与荒漠化是索马里谢贝利河流域等半干旱地区面临的主要环境挑战。本研究利用多时相Landsat影像、CHIRPS降雨数据、Google Earth Engine和GIS技术，评估了2000年至2025年土地退化与荒漠化的脆弱性。通过加权叠加分析，结合NDVI、SAVI、BSI、SI、NDBI、NDMI、MNDWI、LST和降雨量等九项环境指标，构建了荒漠化脆弱性指数(DVI)。结果表明，该流域大部分地区较为脆弱，谢贝利河走廊和南部地区受影响相对较小，而其他地区则呈现植被和环境健康下降的趋势。DVI显示出脆弱性随时间升高的趋势。该指数已有效用于评估空间荒漠化脆弱性，并通过341个参考点进行验证，在2000年、2015年和2025年均表现出良好的区分能力。","Geocarto International","2026-10-14T00:00:00Z",75,{"impact":17,"substance":188,"depth":124,"authority":189,"freshness":255,"relevant":22,"comment":256},9,"基于多时相遥感和DVI指数的半干旱区土地退化评估，方法扎实、数据规模可观，对干旱区农业生态监测有参考价值，但区域局限于索马里，公共影响层级有限。",[258],{"name":251,"url":248},[260,27,28,261,262],"土地退化","Google Earth Engine","干旱区农业",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":265},{"candidates":266,"agricultural_evidence":14,"reason":154},[267,272,278,282,287],{"code":40,"score":268,"supported":42,"title":269,"abstract":270,"tags":271},4,[],[46],[262],{"code":273,"score":73,"supported":42,"title":274,"abstract":275,"tags":277},"I4",[],[276],"GIS",[],{"code":49,"score":205,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[261,28],{"code":283,"score":22,"supported":14,"title":284,"abstract":285,"tags":286},"H1",[],[],[260],{"code":56,"score":22,"supported":14,"title":288,"abstract":289,"tags":290},[],[],[27],[292,293],"Shabelle River Basin 荒漠化","索马里 土地退化 遥感","ShabelleRiverBasin荒漠化-5099","10.1080\u002F10106049.2026.2746835",{"doi":295,"openalex_id":297,"authors":298,"venue":251,"cited_by_count":103,"oa_url":310,"card":311,"direction":108,"ingested_from":110},"W7221831999",[299,302,305,308],{"name":300,"orcid":301},"Ismail Mohamoud Ali","https:\u002F\u002Forcid.org\u002F0009-0008-7590-6428",{"name":303,"orcid":304},"Gabriel Tetteh","https:\u002F\u002Forcid.org\u002F0000-0003-3603-1952",{"name":306,"orcid":307},"Mahad Abdullahi Hussein","https:\u002F\u002Forcid.org\u002F0009-0002-4093-6308",{"name":309,"orcid":9},"Abdirahman Mohamed Nor","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F10106049.2026.2746835?needAccess=true",{"tldr":312,"method":313,"finding":314,"direction":108,"opportunity":315},"基于多时相遥感与GIS构建荒漠化脆弱性指数，评估索马里谢贝利河流域2000-2025年土地退化与荒漠","Landsat影像、CHIRPS降雨、Google Earth Engine与G","流域大部分区域脆弱，DVI呈升高趋势，谢贝利河走廊和南部相对较轻，341个参考点验证效果良好。","可引入机器学习与高分辨率时序数据提升DVI预测精度，并耦合社会经济因素分析驱动机制。","2026-10-16T23:30:30.183766Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":9,"secondary_topics":333,"classification":334,"search_phrases":351,"slug":354,"view_count":22,"doi":355,"paper":356,"created_at":380},5012,"Evaluating Multi-Index Remote Sensing Frameworks for Drought Monitoring in Seasonally Dry Dipterocarp Deciduous Forests: A Case Study of Yok Don National Park, Vietnam","https:\u002F\u002Fdoi.org\u002F10.5755\u002Fj01.erem.82.3.44241","This study examined the effectiveness of the normalized difference vegetation index (NDVI), land surface temperature (LST), and their integrated indices in capturing dry-season drought dynamics in the dipterocarp deciduous forest ecosystems of Yok Don National Park from 2014 to 2024. Results indicate that individual remote-sensing indices captured drought processes but reflected different dimensions of drought conditions, with NDVI indicating canopy greenness and LST detecting early thermal stress. Integrated indices partially mitigated the limitations of single indicators during confirmed drought years by showing stronger correlations with reference data. The effectiveness of the integrated indices varied significantly depending on the integration framework. The spatial-normalization–based integration of NDVI and LST showed consistently strong correlations with surface evapotranspiration (ET), with correlation coefficients of approximately 0.8 across years, and was less sensitive to land-cover heterogeneity. In contrast, the triangular NDVI–LST space integration exhibited variable relationships with ET due to its sensitivity to edge definition and phenological shifts in dipterocarp deciduous forests. Phase-dependent behavior was observed, with integrated indices maintaining more stable relationships with ET during drying periods, while NDVI showed greater sensitivity under wetter conditions. The results also indicate that VHI demonstrated a more robust and stable performance than TVDI, suggesting its suitability as a primary indicator in heterogeneous forest environments. These findings provide practical guidance for drought monitoring in heterogeneous forest landscapes by identifying adaptive integration strategies tailored to different hydroclimatic conditions.","本研究考察了归一化差异植被指数（NDVI）、地表温度（LST）及其综合指数在捕捉2014至2024年 Yok Don 国家公园龙脑香落叶林生态系统旱季干旱动态方面的有效性。结果表明，单一遥感指数能够捕捉干旱过程，但反映的是干旱状况的不同维度，其中NDVI指示冠层绿度，LST探测早期热胁迫。综合指数在已确认的干旱年份中通过与参考数据表现出更强的相关性，部分缓解了单一指标的局限性。综合指数的有效性因集成框架的不同而差异显著。基于空间标准化的NDVI与LST集成与地表蒸散发（ET）表现出持续较强的相关性，各年份相关系数约为0.8，且对土地覆盖异质性较不敏感。相比之下，三角NDVI–LST空间集成与ET的关系多变，这是由于其边缘定义及龙脑香落叶林物候变化较为敏感。研究观察到相位依赖行为，综合指数在干旱期与ET保持更稳定的关系，而NDVI在较湿润条件下表现出更高的敏感性。结果还表明，VHI比TVDI表现出更稳健和稳定的性能，表明其适合作为异质森林环境中的主要指标。这些发现通过识别适应不同水文气候条件的自适应集成策略，为异质森林景观的干旱监测提供了实践指导。","Environmental Research Engineering and Management","2026-10-13T00:00:00Z",67,{"impact":21,"substance":18,"depth":19,"authority":20,"freshness":255,"relevant":22,"comment":327},"越南干旱区遥感监测方法学研究，方法新颖数据扎实，对农业遥感干旱监测有参考价值，但属区域性案例，公共影响有限。",[329],{"name":323,"url":320},[331,129,28,29,332],"农业遥感","森林生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":335},{"candidates":336,"agricultural_evidence":14,"reason":154},[337,341,345],{"code":40,"score":41,"supported":42,"title":338,"abstract":339,"tags":340},[44],[46,44],[29],{"code":49,"score":189,"supported":42,"title":342,"abstract":343,"tags":344},[51],[53],[129,331,28],{"code":149,"score":65,"supported":42,"title":346,"abstract":347,"tags":350},[],[348,349],"蒸散发","evapotranspiration",[],[352,353],"Yok Don 国家公园 遥感 干旱","NDVI LST 干旱监测 森林","YokDon国家公园遥感干旱-5012","10.5755\u002Fj01.erem.82.3.44241",{"doi":355,"openalex_id":357,"authors":358,"venue":323,"cited_by_count":103,"oa_url":374,"card":375,"direction":108,"ingested_from":110},"W7221717828",[359,362,365,367,369,371],{"name":360,"orcid":361},"Mai Son Le","https:\u002F\u002Forcid.org\u002F0000-0002-5360-7119",{"name":363,"orcid":364},"Ha Thu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-4490-308X",{"name":366,"orcid":9},"Hung Manh Nguyen",{"name":368,"orcid":9},"Huy Xuan Chu",{"name":370,"orcid":9},"Hoa Thanh Nguyen",{"name":372,"orcid":373},"Duy-Toan Dao","https:\u002F\u002Forcid.org\u002F0009-0003-6057-4436","https:\u002F\u002Ferem.ktu.lt\u002Findex.php\u002Ferem\u002Farticle\u002Fdownload\u002F44241\u002F23724",{"tldr":376,"method":377,"finding":378,"direction":108,"opportunity":379},"评估NDVI、LST及其集成指数在越南干旱季风落叶林干旱监测中的效果。","利用2014-2024年NDVI、LST遥感数据，构建空间归一化和三角空间集成指","空间归一化集成指数与ET相关性稳定（约0.8），VHI比TVDI更稳健，集成效果依赖框架。","可探索自适应集成框架在不同水热条件和异质景观下的干旱监测普适性。","2026-10-15T23:30:32.336137Z",{"id":382,"title":383,"url":384,"summary":385,"summary_zh":386,"content":9,"source_name":387,"source_url":384,"published_at":388,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":389,"sources":391,"tags":393,"primary_topic":9,"secondary_topics":397,"classification":398,"search_phrases":413,"slug":416,"view_count":73,"doi":417,"paper":418,"created_at":440},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。","PLoS ONE","2026-09-25T00:00:00Z",{"impact":124,"substance":188,"depth":124,"authority":189,"freshness":65,"relevant":22,"comment":390},"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。",[392],{"name":387,"url":384},[27,28,394,395,396],"青藏高原","SIF","生态治理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":399},{"candidates":400,"agricultural_evidence":14,"reason":154},[401,405,409],{"code":64,"score":73,"supported":42,"title":402,"abstract":403,"tags":404},[],[68],[],{"code":56,"score":22,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[27],{"code":49,"score":22,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[28],[414,415],"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","10.1371\u002Fjournal.pone.0359348",{"doi":417,"openalex_id":419,"authors":420,"venue":387,"cited_by_count":103,"oa_url":434,"card":435,"direction":108,"ingested_from":110},"W7214339598",[421,424,427,429,431],{"name":422,"orcid":423},"Zhijian Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-4764-2943",{"name":425,"orcid":426},"Hui Lin","https:\u002F\u002Forcid.org\u002F0000-0003-1278-4351",{"name":428,"orcid":9},"Lei Wu",{"name":430,"orcid":9},"Linling Tang",{"name":432,"orcid":433},"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":436,"method":437,"finding":438,"direction":108,"opportunity":439},"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。","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。","2026-09-28T23:30:30.884081Z",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":447,"source_url":444,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":449,"score_detail":450,"sources":453,"tags":455,"primary_topic":458,"secondary_topics":463,"classification":464,"search_phrases":480,"slug":483,"view_count":73,"doi":484,"paper":485,"created_at":500},3558,"Meteorolojik Kuraklığın Vejetasyon Sağlığı Üzerindeki Mekânsal ve Zamansal Etkilerinin SPI ve NDVI ile Analizi: Gediz Havzası Örneği","https:\u002F\u002Fdoi.org\u002F10.48123\u002Frsgis.1858019","Kuraklık, özellikle yarı kurak iklim koşullarına sahip havzalarda su kaynakları, tarımsal üretim ve ekosistem sürdürülebilirliği üzerinde belirleyici etkiler oluşturan başlıca doğal afetlerden biridir. Bu bağlamda, kuraklığın bitki örtüsü üzerindeki mekânsal ve zamansal etkilerinin bütüncül yaklaşımlarla değerlendirilmesi büyük önem taşımaktadır. Bu çalışmada, meteorolojik kuraklığın vejetasyon sağlığı üzerindeki mekânsal ve zamansal etkileri, Standartlaştırılmış Yağış İndeksi (SPI) ve Normalize Fark Bitki Örtüsü İndeksi (NDVI) kullanılarak Gediz Havzası örneğinde analiz edilmiştir. SPI analizleri 1970–2023 yılları arasındaki uzun dönem yağış verilerine dayanırken, NDVI verileri 2000–2023 dönemine ait MODIS uydu görüntülerinden elde edilmiştir. İki veri seti arasındaki ilişkiler, ortak dönem olan 2000–2023 yılları için incelenmiştir. Elde edilen bulgular, meteorolojik kuraklık ile vejetasyon sağlığı arasındaki ilişkinin mevsimsel olarak değişkenlik gösterdiğini ortaya koymaktadır. En güçlü ilişki yaz mevsiminde gözlenmiş (r ≈ 0.70) ve bu durum vejetasyonun yağış eksikliklerine en duyarlı olduğu dönemin yaz ayları olduğunu göstermiştir. Buna karşılık, kış (r ≈ 0.05) ve ilkbahar (r ≈ 0.02) mevsimlerinde ilişki oldukça zayıf bulunmuştur. Belirlenen kurak yıllarda (2004, 2008 ve 2022) NDVI değerlerinde belirgin düşüşler gözlenmiştir. Sonuç olarak, vejetasyonun kuraklığa verdiği tepkinin yıl boyunca homojen olmadığı, mevsimsel iklim koşulları ve diğer çevresel faktörlere bağlı olarak değiştiği belirlenmiştir.","干旱是主要自然灾害之一，尤其在具有半干旱气候条件的流域中，对水资源、农业生产和生态系统可持续性产生决定性影响。在此背景下，以整体性方法评估干旱对植被的时空影响具有重要意义。本研究以盖迪兹流域为例，利用标准化降水指数（SPI）和归一化植被指数（NDVI）分析了气象干旱对植被健康的时空影响。SPI分析基于1970—2023年的长期降水数据，而NDVI数据则来自2000—2023年期间的MODIS卫星影像。两个数据集之间的关系在共同时段2000—2023年进行了分析。研究结果表明，气象干旱与植被健康之间的关系呈现季节性变化。最显著的关系出现在夏季（r ≈ 0.70），这表明夏季是植被对降水亏缺最为敏感的时期。相比之下，冬季（r ≈ 0.05）和春季（r ≈ 0.02）的关系则非常微弱。在确定的干旱年份（2004年、2008年和2022年），NDVI值出现了明显下降。综上，植被对干旱的响应在全年并非均匀一致，而是随季节性气候条件及其他环境因素而变化。","Turkish Journal of Remote Sensing and GIS","2026-09-24T00:00:00Z",71,{"impact":17,"substance":451,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":452},21,"基于SPI与NDVI长时序数据揭示气象干旱对植被健康影响的季节性差异，方法规范、结论可靠，对农业干旱遥感监测有参考价值，但属区域案例研究，公共影响有限。",[454],{"name":447,"url":444},[129,28,29,456,457],"植被指数","SPI",{"code":40,"slug":459,"name":460,"path":461,"parent_code":462},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":465,"locked":14,"version":35,"method":36,"evidence":466},"classified",{"candidates":467,"agricultural_evidence":42},[468,472,476],{"code":40,"score":268,"supported":42,"title":469,"abstract":470,"tags":471},[],[46],[29],{"code":149,"score":73,"supported":42,"title":473,"abstract":474,"tags":475},[],[152],[],{"code":49,"score":73,"supported":14,"title":477,"abstract":478,"tags":479},[],[],[129,456,28],[481,482],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558","10.48123\u002Frsgis.1858019",{"doi":484,"openalex_id":486,"authors":487,"venue":447,"cited_by_count":103,"oa_url":494,"card":495,"direction":108,"ingested_from":110},"W7214166880",[488,491],{"name":489,"orcid":490},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":492,"orcid":493},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":496,"method":497,"finding":498,"direction":108,"opportunity":499},"用SPI和NDVI分析土耳其盖迪兹流域气象干旱对植被健康的时空影响。","基于1970-2023年降水SPI与2000-2023年MODIS NDVI，做","干旱与植被关系随季节变化，夏季最强(r≈0.70)，冬春极弱；干旱年NDVI明显下降。","可引入滞后效应与多尺度SPI，结合土壤水分和灌溉数据，提升干旱对植被影响的预测能力。","2026-09-26T23:30:31.357310Z"]