[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2941":3,"related-2941":63},{"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":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":62},2941,"Drought dynamics and climatic drivers in the Tarim Basin using remote sensing indices and pixel-wise machine learning","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72142-5","Abstract Drought characterization in hyper-arid endorheic basins requires multi-index approaches that capture distinct hydrometeorological processes. This study investigates spatio-temporal drought dynamics in the Tarim Basin (TB)—China’s largest inland arid region—using two complementary remote sensing indices: the Temperature Vegetation Dryness Index (TVDI) for landscape-scale moisture and the Crop Water Stress Index (CWSI) for agricultural drought. Based on 2000–2024 remote sensing and meteorological data, we employed a pixel-wise Random Forest framework with spatial cross-validation and permutation importance analysis to quantify climatic drivers across the TB. Results reveal a fundamental “core-periphery” dichotomy: TVDI identifies persistent extreme drought in the Taklamakan Desert core, while CWSI reveals alleviating water stress in peripheral oasis farmlands (73.21% showing significant decrease, p \u003C 0.05). Despite regional warming-wetting trends, TVDI exhibited an insignificant decrease (54.72% of the basin), contrasting with CWSI's significant agricultural drought alleviation. Vapor Pressure Deficit (VPD)—a key atmospheric dryness indicator—exhibited high relative permutation importance for both drought indices (72–75%), considerably exceeding the values obtained for precipitation (6–8%) within the Tarim Basin. Secondary drivers diverge by land surface type: TVDI responds to Relative Humidity (8.2%) and Precipitation (6.1%), while CWSI is modulated by Land Surface Temperature (9.4%) and Sunshine Hours (7.8%). Partial correlation analyses controlling for topography and temperature confirm VPD’s independent effect on drought severity. Large-scale climate oscillations, particularly the Arctic Oscillation (AO) and ENSO-PDO interactions, significantly modulate interannual drought variability (r = 0.74–0.75, p \u003C 0.01). This study provides the first pixel-scale quantification of the relative dominance of atmospheric water demand over precipitation in driving drought evolution in the Tarim Basin, with VPD contributing 72–75% of the total permutation importance compared to 6–8% for precipitation. This quantitative benchmark offers actionable parameters for drought monitoring systems in arid regions and underscores the need to integrate VPD and large-scale climate signals into early warning frameworks.","摘要 极端干旱内流盆地的干旱特征刻画需要能够捕捉不同水文气象过程的多指标方法。本研究利用两个互补的遥感指数——用于景观尺度土壤湿度的温度植被干旱指数（TVDI）和用于农业干旱的作物水分胁迫指数（CWSI）——探讨了塔里木盆地（TB）——中国最大的内陆干旱区——干旱的时空动态。基于2000—2024年遥感与气象数据，我们采用逐像元随机森林框架，结合空间交叉验证和置换重要性分析，量化了塔里木盆地气候驱动因子的作用。结果揭示了一种根本性的“核心—边缘”二分格局：TVDI识别出塔克拉玛干沙漠核心区持续存在的极端干旱，而CWSI则显示外围绿洲农田的水分胁迫正在缓解（73.21%呈显著下降，p \u003C 0.05）。尽管区域呈现暖湿化趋势，TVDI却表现出不显著的下降（占流域面积的54.72%），这与CWSI所反映的农业干旱显著缓解形成对比。饱和水汽压差（VPD）——一个关键的大气干燥度指标——对两个干旱指数均表现出较高的相对置换重要性（72%—75%），远超塔里木盆地降水所对应的值（6%—8%）。次要驱动因子因地表类型而异：TVDI响应相对湿度（8.2%）和降水（6.1%），而CWSI受地表温度（9.4%）和日照时数（7.8%）调控。控制地形和温度后的偏相关分析证实了VPD对干旱严重程度的独立影响。大尺度气候振荡，尤其是北极涛动（AO）和ENSO-PDO相互作用，显著调控着年际干旱变率（r = 0.74—0.75，p \u003C 0.01）。本研究首次在像元尺度上量化了大气需水量相对于降水在驱动塔里木盆地干旱演变中的相对主导地位，其中VPD贡献了总置换重要性的72%—75%，而降水仅贡献6%—8%。这一定量基准为干旱区干旱监测系统提供了可操作的参数，并凸显了将VPD和大尺度气候信号纳入预警框架的必要性。",null,"Scientific Reports","2026-09-18T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,8,1,"首次在像元尺度量化VPD对干旱的主导作用，方法新颖、数据跨度长，对干旱预警系统建设有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","气候变化","遥感监测","干旱预警","塔里木盆地",[32,33],"塔里木盆地 遥感 干旱","TVDI CWSI 干旱监测","塔里木盆地遥感干旱-2941",0,"10.1038\u002Fs41598-026-72142-5",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":55,"direction":59,"ingested_from":61},"W7213539719",[40,42,44,46,49,51,53],{"name":41,"orcid":9},"Mutallip Sattar",{"name":43,"orcid":9},"Alim Abbas",{"name":45,"orcid":9},"Sardar Parhat",{"name":47,"orcid":48},"Alimujiang Yasen","https:\u002F\u002Forcid.org\u002F0000-0002-9860-7921",{"name":50,"orcid":9},"Muhemaiti Wahafu",{"name":52,"orcid":9},"Akida Salam",{"name":54,"orcid":9},"Batur Bake",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"基于遥感指数与逐像元机器学习，量化塔里木盆地2000—2024年干旱动态及气候驱动因子。","TVDI与CWSI双指数，逐像元随机森林、空间交叉验证与置换重要性分析。","干旱呈核心—边缘分异，VPD贡献72–75%远超降水的6–8%，主导干旱演变。","农业遥感与作物表型","可将VPD与大尺度气候振荡纳入干旱预警，并拓展至其他干旱内陆盆地的逐像元归因研究。","openalex","2026-09-19T23:30:32.698746Z",{"total":64,"page":21,"page_size":64,"items":65},6,[66,106,147,195,238,286],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":71,"content":9,"source_name":72,"source_url":69,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":75,"sources":80,"tags":82,"search_phrases":85,"slug":88,"view_count":35,"doi":89,"paper":90,"created_at":105},2665,"Assessing historical climatic impacts on wetland change using satellite imagery: a transferable framework demonstrated on the Texas coast","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffmars.2026.1911273","Coastal wetlands provide critical ecosystem services worldwide but are increasingly threatened by climate-driven stressors, including sea-level rise, coastal flooding, drought, wave erosion, and extreme temperature events. Long-term, spatially consistent monitoring is needed to quantify wetland transformation and identify ecosystems most vulnerable to these stressors. The objective of the study was to develop tools that harmonize historical Landsat (5, 7, 8, 9) and Sentinel (1, 2) satellite datasets to map wetlands and identify wetland areas most vulnerable to climate-driven stress, while being readily transferable to other coastal regions. The Texas Coastal Bend Region is presented as a case study to demonstrate the utility of these tools in evaluating wetland change. To do this, the Multi Decadal Wetland Extent Tool (WET-MUD) was developed in Google Earth Engine, along with the Wetland Health Analysis: Multi-Source (WHAMS). WET-MUD is a semi-automated wetland classification tool that produces annual maps with pixels classified as wetland, upland, or water from 1984 to 2024. WHAMS is a Google Earth Engine tool and framework that evaluates changes to wetland plant health (using Normalized Difference Vegetation Index (NDVI) as a proxy) at a monthly level for the 41-year period of record. Total classification accuracy for the Coastal Bend Region of Texas averaged 93% for the 40-year study period, and wetland pixel classification averaged 82%. Annual wetland extent maps show that since 1984, the region has experienced approximately 193 km 2 of net wetland loss, representing 17% of the initial wetland extent, with most of these losses (95%) occurring between 2008 and 2016. Analyses of NDVI for wetland areas show that 43 km 2 of coastal palustrine (low-lying, freshwater) wetlands were especially sensitive to repeated drought events (2008-2010, 2011-2015), representing 22% of the total coastal wetland losses. The publicly available tools developed in this study provide insight into wetland resilience by rapidly measuring both wetland extents and health at a regional, multi-decadal scale in a way that can be reproduced for areas beyond the Coastal Bend of Texas.","沿海湿地为全球提供关键的生态系统服务，但日益受到气候驱动压力的威胁，包括海平面上升、海岸洪水、干旱、波浪侵蚀和极端温度事件。需要长期、空间一致的监测来量化湿地变化并识别最易受这些压力影响的生态系统。本研究的目标是开发工具，以协调历史Landsat（5、7、8、9）和Sentinel（1、2）卫星数据集，从而绘制湿地分布图并识别最易受气候驱动压力影响的湿地区域，同时能够方便地推广到其他沿海地区。本文以德克萨斯州沿海湾岸地区为案例研究，展示这些工具在评估湿地变化方面的实用性。为此，在Google Earth Engine中开发了多年代际湿地范围工具（WET-MUD），以及湿地健康分析：多源工具（WHAMS）。WET-MUD是一种半自动化湿地分类工具，可生成1984年至2024年期间像元被分类为湿地、高地或水体的年度地图。WHAMS是一个Google Earth Engine工具和框架，用于在41年记录期内按月评估湿地植物健康状况的变化（以归一化差异植被指数（NDVI）作为代理指标）。在40年研究期内，德克萨斯州沿海湾岸地区的总分类精度平均为93%，湿地像元分类精度平均为82%。年度湿地范围地图显示，自1984年以来，该地区经历了约193 km²的净湿地损失，占初始湿地范围的17%，其中大部分损失（95%）发生在2008年至2016年间。对湿地区域NDVI的分析表明，43 km²的沿海沼泽湿地（低洼淡水湿地）对反复干旱事件（2008-2010年、2011-2015年）尤为敏感，占沿海湿地总损失的22%。本研究开发的公开可用工具通过快速测量区域多年代际尺度上的湿地范围和健康状况，为理解湿地恢复力提供了见解，其方法可在德克萨斯州沿海湾岸以外的地区重现。","Frontiers in Marine Science","2026-09-15T00:00:00Z",80,{"impact":17,"substance":76,"depth":17,"authority":77,"freshness":78,"relevant":21,"comment":79},22,13,9,"基于Landsat与Sentinel长时序影像构建可迁移湿地制图与健康评估框架，方法新颖、数据规模大，对沿海湿地监测与农业生态信息化有参考价值。",[81],{"name":72,"url":69},[26,27,28,83,84],"湿地保护","海岸带",[86,87],"农业遥感 气候变化 湿地保护 遥感监测","农业遥感 气候变化","农业遥感气候变化湿地保护遥感监测-2665","10.3389\u002Ffmars.2026.1911273",{"doi":89,"openalex_id":91,"authors":92,"venue":72,"cited_by_count":35,"oa_url":99,"card":100,"direction":59,"ingested_from":61},"W7213245622",[93,95,97],{"name":94,"orcid":9},"John Malito",{"name":96,"orcid":9},"Vanessa Valenti",{"name":98,"orcid":9},"Katie Swanson","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fmarine-science\u002Farticles\u002F10.3389\u002Ffmars.2026.1911273\u002Fpdf",{"tldr":101,"method":102,"finding":103,"direction":59,"opportunity":104},"开发可迁移框架，用卫星影像评估历史气候对湿地变化的影响。","Google Earth Engine中构建WET-MUD和WHAMS工具，融合","1984-2024年德州海岸湿地净损失193 km²，95%发生在2008-2016年，淡水湿地对干","可将该可迁移框架应用于其他沿海湿地，结合气候数据量化不同压力源的贡献。","2026-09-16T23:30:29.083410Z",{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":74,"score_detail":114,"sources":116,"tags":118,"search_phrases":122,"slug":125,"view_count":35,"doi":126,"paper":127,"created_at":146},2799,"Multi-Temporal Assessment of Bimodal Monsoon Flood Dynamics and Agricultural Exposure Using Integrated Sentinel-1 SAR and Sentinel-2 Optical Data in Punjab, Pakistan","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fgeohazards7040114","Floods in monsoon-dominated river basins exhibit high spatio-temporal variability, necessitating high-resolution, multi-sensor approaches for reliable monitoring and impact assessment. In flood-prone agricultural regions, continuous monitoring using optical remote sensing is frequently hindered by dense monsoon cloud cover. This study establishes a comprehensive multi-sensor framework within the Google Earth Engine (GEE) to examine the spatio-temporal dynamics and land surface impacts of the 2025 monsoon floods in Punjab, Pakistan. Flood inundation mapping was executed using a 12-day Sentinel-1 Synthetic Aperture Radar (SAR) time series via a dual-threshold change detection methodology. Concurrently, Sentinel-2 imagery facilitated the derivation of land use\u002Fland cover (LULC) changes and vegetation dynamics using a Random Forest classifier, achieving overall accuracy of 93% (pre-flood), 91% (during flood), and 94% (post-flood). These accuracy levels were consistent across all three phases despite spectral confusion between water, saturated soil, and vegetation during peak inundation, indicating consistent classification performance under monsoon conditions. The analysis revealed a distinct bimodal flooding regime, characterized by an early monsoon peak in July–August and a more severe late monsoon peak in August-September. The cumulative maximum flood extent reached 9495.33 km2, with peak single-date inundation reaching 5449 km2. Mapped cropland declined by 6.7% (8181 km2) during peak flooding, with 3.9% (4796 km2) remaining non-cropland by the end of the observation period; 5892 km2 of pre-flood cropland was identified as inundated through spatial intersection. In addition, the Normalized Difference Vegetation Index (NDVI) declined by 28.6%, from 0.28 to 0.20, indicating a substantial reduction in vegetation greenness. Spatial consistency was checked with the United Nations Satellite Centre (UNOSAT) and the Food and Agriculture Organization (FAO), independently collected data showing moderate spatial agreement. The proposed framework is highly scalable for continuous flood monitoring, offering critical insights for disaster management and climate adaptation planning in monsoon regions plagued by data scarcity and persistent cloudiness. The approach is particularly relevant for near-real-time operational monitoring, given its reliance on freely available Sentinel data and cloud-based processing that requires no specialized ground infrastructure.","在季风主导的流域，洪水表现出高度的时空变异性，因此需要高分辨率、多传感器方法来进行可靠监测和影响评估。在易受洪水影响的农业区域，利用光学遥感进行连续监测常常受到季风期密集云层的阻碍。本研究在Google Earth Engine（GEE）中建立了一个综合多传感器框架，以考察2025年巴基斯坦旁遮普省季风洪水的时空动态及其对地表的影 响。利用12天Sentinel-1合成孔径雷达（SAR）时间序列，通过双阈值变化检测方法进行洪水淹没制图。同时，利用Sentinel-2影像，通过随机森林分类器提取土地利用\u002F土地覆盖（LULC）变化和植被动态，总体精度分别达到93%（洪水前）、91%（洪水期间）和94%（洪水后）。尽管在淹没峰值期水体、饱和土壤和植被之间存在光谱混淆，这三个阶段的精度水平仍保持一致，表明在季风条件下分类性能稳定。分析揭示出明显的双峰洪水情势，其特征是7—8月季风早期峰值和8—9月更为严重的季风晚期峰值。累计最大洪水范围达到9495.33 km²，单日最大淹没面积达到5449 km²。在洪水峰值期，制图耕地减少6.7%（8181 km²），到观测期末仍有3.9%（4796 km²）为非耕地；通过空间交集识别出5892 km²的洪水前耕地被淹没。此外，归一化植被指数（NDVI）下降28.6%，从0.28降至0.20，表明植被绿度显著降低。利用联合国卫星中心（UNOSAT）和联合国粮食及农业组织（FAO）独立收集的数据进行了空间一致性检验，结果显示具有中等空间一致性。所提出的框架对于连续洪水监测具有高度可扩展性，为受数据稀缺和持续多云困扰的季风地区的灾害管理和气候适应规划提供了关键见解。鉴于该方法依赖免费可用的Sentinel数据和无需专门地面基础设施的云端处理，它尤其适用于近实时业务化监测。","GeoHazards","2026-09-16T00:00:00Z",{"impact":17,"substance":76,"depth":17,"authority":77,"freshness":78,"relevant":21,"comment":115},"基于Sentinel-1\u002F2多源遥感与GEE平台的洪水-农业暴露评估，方法可迁移、数据详实，对农业灾害遥感监测有参考价值，但属区域性案例研究，非国内三农政策或产业级事件。",[117],{"name":112,"url":109},[26,28,119,120,121],"洪涝灾害","作物受灾评估","巴基斯坦",[123,124],"作物受灾评估 农业遥感 巴基斯坦 洪涝灾害","作物受灾评估 农业遥感","作物受灾评估农业遥感巴基斯坦洪涝灾害-2799","10.3390\u002Fgeohazards7040114",{"doi":126,"openalex_id":128,"authors":129,"venue":112,"cited_by_count":35,"oa_url":109,"card":141,"direction":59,"ingested_from":61},"W7213432591",[130,132,135,138],{"name":131,"orcid":9},"Nida Khursheed",{"name":133,"orcid":134},"Asif Sajjad","https:\u002F\u002Forcid.org\u002F0000-0003-1921-8213",{"name":136,"orcid":137},"Mazhar Iqbal","https:\u002F\u002Forcid.org\u002F0000-0001-5891-9798",{"name":139,"orcid":140},"Rana Waqar Aslam","https:\u002F\u002Forcid.org\u002F0000-0002-8711-8700",{"tldr":142,"method":143,"finding":144,"direction":59,"opportunity":145},"基于GEE融合Sentinel-1\u002F2监测2025年巴基斯坦旁遮普季风洪水双峰动态及农田暴露。","GEE平台、Sentinel-1 SAR双阈值变化检测、Sentinel-2随机","洪水呈7-8月与8-9月双峰，最大淹没9495平方公里，耕地减少6.7%，NDVI降28.6%。","可延伸至多云区近实时洪涝-作物损失耦合评估与灾后恢复监测模型构建。","2026-09-17T23:30:37.558541Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":158,"tags":160,"search_phrases":164,"slug":167,"view_count":35,"doi":168,"paper":169,"created_at":194},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",77,{"impact":156,"substance":76,"depth":17,"authority":77,"freshness":78,"relevant":21,"comment":157},15,"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。",[159],{"name":153,"url":150},[26,161,162,163,28],"机器学习","可持续发展","土地退化",[165,166],"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":168,"openalex_id":170,"authors":171,"venue":153,"cited_by_count":35,"oa_url":188,"card":189,"direction":59,"ingested_from":61},"W7213298151",[172,174,177,180,182,185],{"name":173,"orcid":9},"Mandisa Zameko",{"name":175,"orcid":176},"Kgabo Humphrey Thamaga","https:\u002F\u002Forcid.org\u002F0000-0002-2305-9975",{"name":178,"orcid":179},"Mthunzi Mndela","https:\u002F\u002Forcid.org\u002F0000-0002-2384-6856",{"name":181,"orcid":9},"Matthieu Tshanga",{"name":183,"orcid":184},"Nobert Tafadzwa Mukomberanwa","https:\u002F\u002Forcid.org\u002F0009-0003-1896-9813",{"name":186,"orcid":187},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2665972726004137\u002Fpdf",{"tldr":190,"method":191,"finding":192,"direction":59,"opportunity":193},"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":196,"title":197,"url":198,"summary":199,"summary_zh":200,"content":9,"source_name":201,"source_url":198,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":202,"score_detail":203,"sources":207,"tags":209,"search_phrases":213,"slug":216,"view_count":35,"doi":217,"paper":218,"created_at":237},2769,"Climate change impacts and resilience pathways in somalia through a systematic review","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09538-5","Somalia is among the most climate-vulnerable countries worldwide, characterized by predominantly arid and semi-arid regions increasingly affected by recurrent droughts, irregular precipitation, rising temperatures, land degradation, and water scarcity, all of which compromise livelihoods, food security, and socio-economic stability. This study addresses the limited synthesis of recent, spatially explicit evidence on climate change impacts in Somalia by bringing together current literature and institutional reports to analyze the effects of climate change and identify adaptation and resilience strategies The review is entirely desk-based and utilizes 30 secondary data articles from reputable sources such as FAO-SWALIM, the World Bank, ICPAC, FSNAU, USAID, and peer-reviewed research articles from Scopus and web of science published from 2008 to 2025. The results showed that over 60% of Somalia’s territory is categorized as arid or hyper-arid, with the southern parts seeing the most acute soil erosion and environmental deterioration. Significant drought occurrences, especially in 2010–2011 and 2016–2017, were greatly impacted by extensive climatic factors including ENSO and the Indian Ocean Dipole. Climate variability has resulted in reduced crop yields, pasture degradation, livestock mortality, persistent water shortages, and extensive food insecurity, while inadequate governance, restricted climate financing, and deficient early-warning and monitoring systems hinder effective adaptation. The review emphasizes that climate change presents a systemic threat to Somalia’s environmental and livelihood systems, highlighting the necessity for integrated, spatially informed strategies that incorporate GIS-based environmental monitoring, sustainable land and water management, climate-smart agriculture, strengthened institutions, and enhanced access to climate finance to support long-term resilience and sustainable development.","索马里是全球最易受气候变化影响的国家之一，其国土以干旱和半干旱地区为主，日益受到反复出现的干旱、降水不规律、气温上升、土地退化及水资源短缺的影响，这些问题共同威胁着生计、粮食安全和社会经济稳定。本研究针对索马里气候变化影响近期空间明确证据综合不足的问题，汇集当前文献和机构报告，分析气候变化的影响并识别适应与韧性策略。本综述完全基于案头研究，使用了来自FAO-SWALIM、世界银行、ICPAC、FSNAU、USAID等知名来源的30篇二手数据文献，以及2008年至2025年间发表于Scopus和Web of Science的同行评审研究论文。结果表明，索马里超过60%的领土被归类为干旱或极度干旱，南部地区土壤侵蚀和环境退化最为严重。重大干旱事件，尤其是2010—2011年和2016—2017年的干旱，受到包括厄尔尼诺-南方涛动（ENSO）和印度洋偶极子（IOD）在内的广泛气候因素的显著影响。气候变率导致作物减产、牧场退化、牲畜死亡、持续的水资源短缺和大范围的粮食不安全，而治理不足、气候融资受限以及预警和监测系统薄弱则阻碍了有效适应。本综述强调，气候变化对索马里的环境和生计系统构成系统性威胁，凸显了采取综合性、空间信息化策略的必要性，这些策略应包括基于GIS的环境监测、可持续土地和水资源管理、气候智慧型农业、强化制度以及增强气候融资渠道，以支持长期韧性和可持续发展。","Discover Applied Sciences",76,{"impact":17,"substance":204,"depth":205,"authority":77,"freshness":20,"relevant":21,"comment":206},20,17,"系统综述整合2008-2025年30篇文献，揭示索马里干旱与粮食安全影响并提出GIS监测与气候智慧农业等韧性路径，对干旱区农业信息化有参考价值。",[208],{"name":201,"url":198},[210,27,211,28,212],"粮食安全","气候智慧农业","干旱监测",[214,215],"气候智慧农业 干旱监测 气候变化 粮食安全","气候智慧农业 干旱监测","气候智慧农业干旱监测气候变化粮食安全-2769","10.1007\u002Fs42452-026-09538-5",{"doi":217,"openalex_id":219,"authors":220,"venue":201,"cited_by_count":35,"oa_url":198,"card":230,"direction":236,"ingested_from":61},"W7213257664",[221,223,225,228],{"name":222,"orcid":9},"Abdiaziz Hassan Nur",{"name":224,"orcid":9},"Mohamed Osman Abdulkadir",{"name":226,"orcid":227},"Omar Ali","https:\u002F\u002Forcid.org\u002F0009-0001-3009-1850",{"name":229,"orcid":9},"Ahmed Sodal Asir",{"tldr":231,"method":232,"finding":233,"direction":234,"opportunity":235},"系统综述索马里气候变化影响与韧性路径，基于30篇文献与机构报告。","文献综述，采用FAO-SWALIM、世行等2008-2025年30篇二手数据。","超60%国土干旱，干旱致减产、缺水与粮食不安全，治理与预警不足阻碍适应。","农业绿色发展与碳","可结合GIS与遥感构建索马里干旱预警和气候智慧型农业适应决策模型。","智慧农业 \u002F 农业物联网","2026-09-17T23:30:10.557669Z",{"id":239,"title":240,"url":241,"summary":242,"summary_zh":243,"content":9,"source_name":244,"source_url":241,"published_at":73,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":250,"tags":252,"search_phrases":256,"slug":259,"view_count":35,"doi":260,"paper":261,"created_at":285},2674,"Bi-decadal drought assessment in Northwestern Algeria: Integrating meteorological and remote sensing indices","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-026-06526-y","Abstract Drought is an escalating hazard in arid and semi‑arid regions with significant implications for agriculture, ecosystems, and water resources. This study presents a 2003–2023 integrated assessment of meteorological and vegetation-based drought across northwest Algeria, using the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset and Moderate Resolution Imaging Spectroradiometer (MODIS) remotely‑sensed products. Meteorological drought was quantified with the Standardized Precipitation Index (SPI) approximated using standardized precipitation anomalies, at 3‑, 6‑, and 12‑month timescales, whereas vegetation and thermal stress were assessed with MODIS‑derived Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Temporal trends were evaluated using the Mann–Kendall test and Sen’s slope estimator, and relationships between precipitation and vegetation were examined with Pearson correlation. We identified recurrent drought episodes in 2007–2009, 2011–2012, and a pronounced dry phase from 2020–2023. Mann–Kendall results indicated widespread drying across all SPI timescales, with 56% of the study area showing significant negative trends at SPI‑3 (mean Sen’s slope = − 0.10 yr⁻ 1 ). Vegetation indices mirrored these changes, with VHI showing substantially more degraded area than improvement (4.89% vs 0.51% of the domain), while VCI and TCI responses were spatially heterogeneous. The correlation analysis showed moderate but statistically significant relationships between SPI and VHI at the short-term scale (SPI-3 vs. VHI, r = 0.567, p = 0.007), followed by SPI-6 ( r = 0.509, p = 0.019), indicating that vegetation response is more strongly associated with short- to medium-term precipitation variability. These results demonstrate the value of combining meteorological and satellite vegetation indices for regional drought monitoring and early warning, and underscore an ongoing shift toward increased aridity with implications for water management and agricultural adaptation.","摘要 干旱是干旱和半干旱地区日益加剧的灾害，对农业、生态系统和水资源具有重大影响。本研究利用气候灾害组红外降水与站点数据（CHIRPS）数据集和中分辨率成像光谱仪（MODIS）遥感产品，对阿尔及利亚西北部2003—2023年气象干旱和基于植被的干旱进行了综合评估。气象干旱采用标准化降水指数（SPI）量化，该指数通过标准化降水距平在3个月、6个月和12个月时间尺度上近似计算；植被和热胁迫则通过MODIS衍生的植被状态指数（VCI）、温度状态指数（TCI）和植被健康指数（VHI）进行评估。采用Mann–Kendall检验和Sen斜率估计器评估时间趋势，并使用Pearson相关分析检验降水与植被之间的关系。我们识别出2007—2009年、2011—2012年的反复干旱事件，以及2020—2023年显著的干旱阶段。Mann–Kendall结果表明，所有SPI时间尺度上均呈现大范围干旱化趋势，研究区56%的面积在SPI-3上表现出显著负趋势（Sen斜率均值 = −0.10 yr⁻¹）。植被指数反映了这些变化，VHI显示退化面积远大于改善面积（分别占研究区的4.89%和0.51%），而VCI和TCI的响应在空间上具有异质性。相关分析表明，SPI与VHI在短期尺度上存在中等但统计显著的关系（SPI-3与VHI，r = 0.567，p = 0.007），其次为SPI-6（r = 0.509，p = 0.019），表明植被响应与短期至中期降水变率的关系更为密切。这些结果证明了结合气象指标和卫星植被指数进行区域干旱监测与预警的价值，并凸显了干旱化持续加剧的趋势及其对水资源管理和农业适应的影响。","Theoretical and Applied Climatology",71,{"impact":247,"substance":248,"depth":205,"authority":77,"freshness":20,"relevant":21,"comment":249},12,21,"基于CHIRPS与MODIS的阿尔及利亚西北部20年干旱综合评估，方法整合气象与遥感指标，结论对区域干旱监测与农业适应有参考价值，但属境外区域研究，国内产业影响有限。",[251],{"name":244,"url":241},[253,254,28,255,29],"农业气象","水资源管理","植被指数",[257,258],"水资源管理 农业气象 干旱预警 植被指数","水资源管理 农业气象","水资源管理农业气象干旱预警植被指数-2674","10.1007\u002Fs00704-026-06526-y",{"doi":260,"openalex_id":262,"authors":263,"venue":244,"cited_by_count":35,"oa_url":241,"card":280,"direction":59,"ingested_from":61},"W7213346236",[264,267,270,273,275,278],{"name":265,"orcid":266},"Ramzi Benhizia","https:\u002F\u002Forcid.org\u002F0000-0002-8967-2051",{"name":268,"orcid":269},"Brahim Abdelkebir","https:\u002F\u002Forcid.org\u002F0000-0002-8761-8537",{"name":271,"orcid":272},"Behnam Ata","https:\u002F\u002Forcid.org\u002F0000-0002-9690-7269",{"name":274,"orcid":9},"Mukovhe Vele Singo",{"name":276,"orcid":277},"Kwanele Phinzi","https:\u002F\u002Forcid.org\u002F0000-0003-1865-7011",{"name":279,"orcid":9},"György Szabó",{"tldr":281,"method":282,"finding":283,"direction":59,"opportunity":284},"整合气象与遥感指数评估阿尔及利亚西北部2003–2023年干旱演变。","CHIRPS降水与MODIS植被指数，SPI、VCI、TCI、VHI，Mann-","全区普遍变干，SPI-3显著负趋势占56%，VHI退化面积远大于改善，短期降水与植被相关性最强。","可引入机器学习融合多源遥感与气象数据，构建区域干旱预警与作物适应性决策模型。","2026-09-16T23:30:30.795285Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":297,"tags":299,"search_phrases":303,"slug":306,"view_count":35,"doi":307,"paper":308,"created_at":323},2429,"Türkiye’de durağan orman alanlarında 2001 ve 2025 yılları arasında yer yüzey fenolojilerindeki değişimlerin incelenmesi","https:\u002F\u002Fdoi.org\u002F10.53516\u002Fajfr.1970696","Giriş ve Hedefler Orman alanlarındaki yer yüzey fenolojilerinin (YYF) değişiminin izlenmesi iklim değişikliğine uyum ve sürdürülebilir doğal kaynak yönetimi açısından oldukça önemlidir. Bu çalışmada Türkiye’de bulunan durağan orman alanlarındaki 2001-2025 yılları arasında YYF parametreleri 250 m çözünürlüklü MODIS uydu görüntülerinden hesaplanan normalize fark bitki indeksi (NDVI) yardımıyla incelenmiştir. Yöntemler CORINE arazi örtüsü ve Hansen orman kaybı verileriyle belirlenen durağan geniş yapraklı (GY), iğne yapraklı (İY) ve karışık (KA) orman alanlarında yaklaşık iki milyon piksel analiz edilmiştir. Ham NDVI zaman serileri Whittaker filtresi ve çift lojistik regresyonla yeniden oluşturulmuştur. Büyüme mevsimi başlangıcı (BMB), büyüme mevsimi sonu (BMS), büyüme mevsimi uzunluğu (BMU), büyüme mevsimi zirve günü (BMZ), zirve NDVI (zNDVI) ve fenolojik eğri alanı (FEA) parametreleri dinamik mevsimsel genlik yöntemiyle hesaplanmıştır. Trendler Mann-Kendall ve Sen’s slope yöntemleriyle değerlendirilmiştir. YYF parametrelerinin iklim koşullarıyla ilişkisi mevsimsel ortalama sıcaklık ve toplam yağış değişkenleriyle korelasyon analizi yapılarak değerlendirilmiştir. Bulgular GY\u002FİY\u002FKA sınıflarında yılın günü biriminde (day of year), ortalama BMB 105\u002F94\u002F99, BMS 299\u002F298\u002F302, BMU 194\u002F204\u002F203 ve BMZ 186\u002F197\u002F193 olarak bulunmuştur. Büyüme mevsimi evrelerine ilişkin YYF parametrelerinde anlamlı trend oranları görece sınırlı kalırken, zNDVI ve FEA’da sırasıyla %52 ve %27 oranında anlamlı pozitif trend belirlenmiştir. İY ve KA ormanlarda BMS ilerleme eğilimi gösterirken, GY ormanlarda BMS’nin erkene kayması dikkat çekmektedir. YYF parametrelerinin özellikle mevsimsel sıcaklık koşullarıyla yakından ilişkili olduğu görülmüştür.","引言与目标：监测森林区域地表物候（YYF）变化，对于适应气候变化和可持续自然资源管理具有重要意义。本研究利用250 m分辨率的MODIS卫星影像计算归一化植被指数（NDVI），分析了土耳其稳定森林区域2001—2025年间的地表物候参数。方法：基于CORINE土地覆盖数据和Hansen森林损失数据确定的稳定阔叶林（GY）、针叶林（İY）和混交林（KA）区域，分析了约200万个像元。原始NDVI时间序列通过Whittaker滤波和双逻辑斯蒂回归重建。生长季始期（BMB）、生长季末期（BMS）、生长季长度（BMU）、生长季峰值日（BMZ）、峰值NDVI（zNDVI）和物候曲线面积（FEA）参数采用动态季节振幅法计算。趋势采用Mann-Kendall和Sen’s slope方法评估。地表物候参数与气候条件的关系通过其与季节平均气温和总降水量变量的相关分析进行评估。结果：在GY\u002FİY\u002FKA类别中，以年积日（day of year）为单位，平均BMB分别为105\u002F94\u002F99，BMS分别为299\u002F298\u002F302，BMU分别为194\u002F204\u002F203，BMZ分别为186\u002F197\u002F193。生长季各阶段的地表物候参数中显著趋势比例相对有限，而zNDVI和FEA中分别检测到52%和27%的显著正趋势。İY和KA森林中BMS呈推迟趋势，而GY森林中BMS提前值得关注。地表物候参数尤其与季节性温度条件密切相关。","Anadolu Orman Araştırmaları Dergisi","2026-09-11T00:00:00Z",65,{"impact":20,"substance":204,"depth":205,"authority":247,"freshness":20,"relevant":21,"comment":296},"基于MODIS NDVI长时序数据对土耳其稳定林地的植被物候变化进行量化分析，方法规范、数据规模大，属遥感生态监测领域，但与国内三农及农业信息化主题关联较弱，公共价值有限。",[298],{"name":292,"url":289},[27,300,28,301,302],"NDVI","森林生态","植被物候",[304,305],"森林生态 植被物候 气候变化 遥感监测","森林生态 植被物候","森林生态植被物候气候变化遥感监测-2429","10.53516\u002Fajfr.1970696",{"doi":307,"openalex_id":309,"authors":310,"venue":292,"cited_by_count":35,"oa_url":317,"card":318,"direction":59,"ingested_from":61},"W7212250137",[311,314],{"name":312,"orcid":313},"Ömer Faruk Atiz","https:\u002F\u002Forcid.org\u002F0000-0001-6180-7121",{"name":315,"orcid":316},"Süleyman Savaş DURDURAN","https:\u002F\u002Forcid.org\u002F0000-0003-0509-4037","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F6108437",{"tldr":319,"method":320,"finding":321,"direction":59,"opportunity":322},"分析土耳其稳定森林2001-2025年地表物候变化，基于MODIS NDVI数据。","MODIS NDVI、Whittaker滤波、双逻辑回归、Mann-Kendal","zNDVI和FEA显著正趋势，阔叶林生长季结束提前，针叶林和混交林推迟。","可结合多源遥感与气候数据，探究森林物候对极端气候事件的响应机制。","2026-09-14T23:30:26.111459Z"]