[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3558":3,"related-3558":56},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},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。",null,"Turkish Journal of Remote Sensing and GIS","2026-09-24T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"NDVI","遥感监测","干旱监测","植被指数","SPI",[33,34],"Gediz Havzası SPI NDVI","气象干旱 植被健康 遥感","GedizHavzasıSPINDVI-3558",0,"10.48123\u002Frsgis.1858019",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":47,"card":48,"direction":52,"ingested_from":54},"W7214166880",[41,44],{"name":42,"orcid":43},"Kemal Yurddaş","https:\u002F\u002Forcid.org\u002F0000-0003-4691-4038",{"name":45,"orcid":46},"Murat Karabulut","https:\u002F\u002Forcid.org\u002F0000-0002-1456-6908","https:\u002F\u002Fdergipark.org.tr\u002Ftr\u002Fdownload\u002Farticle-file\u002F5578303",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"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。","农业遥感与作物表型","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。","openalex","2026-09-26T23:30:31.357310Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,109,148,191,229,264],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":72,"tags":74,"search_phrases":77,"slug":80,"view_count":36,"doi":81,"paper":82,"created_at":108},3189,"Influence of Drought Events on Vegetation and Land Use Dynamics Utilising Orbital Data: A Case Study in the Pajeú River Basin, Pernambuco","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i03.269030","Monitoring drought in the northeastern Semi-Arid region is a challenge compounded by high rainfall variability and a scarcity of in situ monitoring networks, which makes the use of geotechnologies for water resource management essential. This study aimed to analyze land use and occupation dynamics from 2002 to 2022 in the Pajeú River basin in Pernambuco, Brazil, and their relationship with drought events, using indices based on remote sensing. The Standardized Precipitation Index (SPI) was applied to characterize meteorological drought, while the Vegetation Health Index (VHI) and Normalized Vegetation Water Supply Index (NVSWI) were used to assess the ecosystem's response. The results indicated that the period from 2011 to 2016 was the most severe and prolonged drought, which was reinforced by low VHI and NVSWI values, indicating water stress on vegetation during the same period. The correlation analysis revealed that land use classes, such as vegetation and water, responded directly to drought, whereas degraded anthropogenic areas, including pasture and exposed soil, exhibited the opposite behavior. The joint analysis of the three indices and land use and occupation data provided a comprehensive understanding of the evolution of drought in the study area, highlighting the importance of a multiple approach to monitoring complex environments.","监测半干旱东北部地区的干旱是一项挑战，降雨变率大且原位监测网络稀缺使这一挑战更加复杂，因此利用地理技术进行水资源管理至关重要。本研究旨在利用基于遥感的指数，分析2002年至2022年巴西伯南布哥州帕热乌河流域的土地利用与覆盖动态及其与干旱事件的关系。采用标准化降水指数（SPI）表征气象干旱，同时使用植被健康指数（VHI）和归一化植被供水指数（NVSWI）评估生态系统的响应。结果表明，2011年至2016年是最严重且持续时间最长的干旱期，同期较低的VHI和NVSWI值进一步证实了这一点，表明植被在此期间受到水分胁迫。相关性分析显示，植被和水体等土地利用类别对干旱有直接响应，而牧场和裸露土壤等退化的人为区域则表现出相反的行为。三个指数与土地利用及覆盖数据的联合分析为理解研究区域干旱演变提供了全面的认识，凸显了多方法途径在监测复杂环境中的重要性。","Journal of Hyperspectral Remote Sensing","2026-09-19T00:00:00Z",61,{"impact":21,"substance":69,"depth":70,"authority":17,"freshness":21,"relevant":22,"comment":71},18,15,"该研究利用遥感指数分析巴西半干旱流域干旱与植被动态，方法扎实但属区域性案例，对国内三农信息化参考价值有限。",[73],{"name":65,"url":62},[75,28,76,29,30],"水资源管理","土地利用",[78,79],"Pajeú River basin 干旱 遥感","SPI VHI NVSWI 植被","PajeúRiverbasin干旱遥感-3189","10.29150\u002Fjhrs.v16i03.269030",{"doi":81,"openalex_id":83,"authors":84,"venue":65,"cited_by_count":36,"oa_url":102,"card":103,"direction":52,"ingested_from":54},"W7213792298",[85,88,91,94,97,99],{"name":86,"orcid":87},"Juliana Farias Santos de Moraes","https:\u002F\u002Forcid.org\u002F0000-0002-3241-844X",{"name":89,"orcid":90},"Estephania Silva Jovino","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3533",{"name":92,"orcid":93},"Alex Vinícius de Melo Vieira","https:\u002F\u002Forcid.org\u002F0009-0002-5204-3734",{"name":95,"orcid":96},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":98,"orcid":9},"Sylvana Sylvana Melo dos Santos",{"name":100,"orcid":101},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998","https:\u002F\u002Fperiodicos.ufpe.br\u002Frevistas\u002Fjhrs\u002Farticle\u002Fdownload\u002F269030\u002F52814",{"tldr":104,"method":105,"finding":106,"direction":52,"opportunity":107},"利用遥感指数分析巴西Pajeú河流域2002-2022年干旱对植被与土地利用的影响。","采用SPI、VHI和NVSWI指数，结合遥感数据与土地利用分类。","2011-2016年干旱最严重，植被和水体响应直接，而牧场和裸地呈相反趋势。","可融合多源遥感与机器学习，构建半干旱区干旱-植被-土地利用耦合预警模型。","2026-09-22T23:30:26.557816Z",{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":121,"tags":123,"search_phrases":127,"slug":130,"view_count":36,"doi":131,"paper":132,"created_at":147},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección","2026-09-25T00:00:00Z",72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":119,"relevant":22,"comment":120},9,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[122],{"name":115,"url":112},[124,125,27,28,126],"Sentinel-2","机器学习","建成区提取",[128,129],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":131,"openalex_id":133,"authors":134,"venue":115,"cited_by_count":36,"oa_url":112,"card":141,"direction":146,"ingested_from":54},"W7214385607",[135,138],{"name":136,"orcid":137},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":139,"orcid":140},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":142,"method":143,"finding":144,"direction":52,"opportunity":145},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":153,"content":9,"source_name":154,"source_url":151,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":155,"score_detail":156,"sources":159,"tags":161,"search_phrases":165,"slug":168,"view_count":36,"doi":169,"paper":170,"created_at":190},3551,"Diachronic Assessment of Water Springs in Ribat El Kheir Plateau (Morocco), under Climatic and Anthropogenic Pressure, Using Remote Sensing (1985-2025)","https:\u002F\u002Fdoi.org\u002F10.4028\u002Fp-9o0ojj","The Ribat El Kheir plateau, located in the northern part of the Tabular Middle Atlas (Morocco), is characterized by carbonate formations (Liasic dolomitic limestones) and a dense network of tectonic faults. This study conducts a diachronic assessment of water springs and their interaction with climatic conditions and anthropogenic factors related to the expansion of irrigated agriculture (farms) from 1985 to 2025. A multi-source methodological approach was adopted, combining the inventory and mapping of springs, the application of remote sensing, using Landsat and Sentinel satellite imagery, and the calculation of standard indices, including the Normalized Difference Water Index (NDWI), the Normalized Difference Vegetation Index (NDVI), and the Standardized Precipitation Index (SPI). The results indicate that a combination of geomorphological context primarily influences the spatial distribution of the 101 inventoried springs, the lithological nature of the formations (permeable dolomites), structural features, and climatic factors. However, a significant regression in both the number and flow of these springs has been observed. Concurrently, irrigated fruit agriculture has expanded remarkably over the study period. The SPI analysis further reveals a climatic trend towards increased aridity. These findings highlight the critical and synergistic pressure of climate change and intensive agriculture on water resources in this vulnerable semi-arid region.","位于摩洛哥中阿特拉斯板状山脉北部的里巴特·埃尔·海尔（Ribat El Kheir）高原，以碳酸盐岩地层（里阿斯统白云质灰岩）和密集的构造断裂网络为特征。本研究对1985年至2025年间泉水动态及其与气候条件和灌溉农业（农场）扩张相关人为因素的相互作用进行了历时性评估。研究采用多源方法学途径，结合泉水编录与制图、遥感技术应用（利用Landsat和Sentinel卫星影像）以及标准指数的计算，包括归一化差异水体指数（NDWI）、归一化差异植被指数（NDVI）和标准化降水指数（SPI）。结果表明，101处编录泉水的空间分布主要受地貌背景、地层岩性特征（渗透性白云岩）、构造特征及气候因素的综合影响。然而，观测到这些泉水的数量和流量均出现显著衰退。与此同时，灌溉果树农业在研究期内显著扩张。SPI分析进一步揭示了气候趋于干旱化的趋势。这些发现凸显了气候变化与集约化农业对这一脆弱半干旱地区水资源的临界协同压力。","International journal of engineering research in Africa",67,{"impact":21,"substance":157,"depth":19,"authority":20,"freshness":119,"relevant":22,"comment":158},20,"基于Landsat与Sentinel遥感的40年泉水退化评估，方法扎实、数据翔实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[160],{"name":154,"url":151},[162,27,28,163,164],"农业水资源","灌溉农业","干旱化",[166,167],"Ribat El Kheir 泉水 遥感","摩洛哥 灌溉农业 水资源","RibatElKheir泉水遥感-3551","10.4028\u002Fp-9o0ojj",{"doi":169,"openalex_id":171,"authors":172,"venue":154,"cited_by_count":36,"oa_url":9,"card":185,"direction":52,"ingested_from":54},"W7214294130",[173,175,176,179,181,182],{"name":174,"orcid":9},"Hamid Achiban",{"name":174,"orcid":9},{"name":177,"orcid":178},"Miloud Afenzar","https:\u002F\u002Forcid.org\u002F0009-0008-0839-0230",{"name":180,"orcid":9},"Hassan Achiban",{"name":180,"orcid":9},{"name":183,"orcid":184},"Nourddine El Gali","https:\u002F\u002Forcid.org\u002F0009-0001-7025-4765",{"tldr":186,"method":187,"finding":188,"direction":52,"opportunity":189},"基于遥感和指数分析，评估摩洛哥Ribat El Kheir高原1985-2025年泉水数量与流量的退","Landsat\u002FSentinel影像、NDWI\u002FNDVI\u002FSPI指数、泉水编目与","泉水数量与流量显著减少，灌溉农业扩张与气候干旱化协同加剧水资源压力。","可结合多源遥感与机器学习，量化灌溉扩张对泉水流量的贡献，并预测未来情景。","2026-09-26T23:30:29.568284Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":199,"score_detail":200,"sources":202,"tags":204,"search_phrases":208,"slug":211,"view_count":36,"doi":212,"paper":213,"created_at":228},3483,"АГРОМЕТЕОРОЛОГІЧНІ УМОВИ ТА ДИНАМІКА ВЕГЕТАЦІЙНИХ ІНДЕКСІВ ПШЕНИЦІ ОЗИМОЇ У ПІВДЕННОМУ СТЕПУ УКРАЇНИ","https:\u002F\u002Fdoi.org\u002F10.32848\u002Fagrar.innov.2026.37.23","Комплексно оцінити агрометеорологічні умови вегетаційних періодів 2021–2025 рр. для пшениці озимої в умовах Південного Степу України та встановити закономірності динаміки вегетаційних індексів NDVI, NDRE і NDMI під впливом сезонної мінливості температурного режиму, опадів і дефіциту вологи. Методи. Дослідження проведено на базі Навчально-науково- практичного центру Миколаївського національного аграрного університету. Використано метеорологічні дані автоматичної станції Pessl Instruments (iMETOS) за 2021–2025 рр. та супутникові дані Sentinel‑2 SR Harmonized, оброблені в середовищі Google Earth Engine. Для оцінки стану посівів розраховано індекси NDVI, NDRE і NDMI та виконано їх порівняльний аналіз у взаємозв’язку з температурним режимом, кількістю опадів і перебігом основних фенологічних фаз розвитку пшениці озимої. Результати. Встановлено істотні міжрічні відмінності агрометеорологічних умов, які визначали характер формування та сезонної динаміки вегетаційних індексів. Найсприятливішими для розвитку рослин були вегетаційні періоди 2021–2022 та 2022–2023 рр., коли значення NDVI досягали 0,8–0,9, а NDRE – 0,6–0,7. Вегетаційний період 2023–2024 рр. характеризувався пригніченим осіннім розвитком і компенсаторним весняним наростанням біомаси, тоді як у 2024–2025 рр. добрі умови осінньої вегетації не забезпечили тривалого весняного розвитку через холодну й відносно суху весну. Встановлено, що найбільш критичним періодом розвитку водного стресу був червень, коли рослини переходили від фази колосіння до наливу зерна. Індекс NDMI найраніше реагував на погіршення водного режиму посівів і був найбільш чутливим індикатором розвитку водного стресу, тоді як скорочення тривалості плато NDVI відображало обмеження реалізації продуктивного потенціалу посівів. Висновки. Динаміка вегетаційних індексів пшениці озимої тісно пов’язана з особливостями сезонного перебігу температурного режиму та зволоження. Вирішальне значення для формування фізіологічного стану рослин мали умови весняного періоду, а найбільш критичним щодо розвитку водного стресу був перехід від колосіння до наливу зерна. Індекс NDMI доцільно використовувати як ранній індикатор водного стресу, тоді як комплексне застосування NDVI, NDRE і NDMI забезпечує ефективний моніторинг стану посівів, а скорочення тривалості плато NDVI може бути використане як індикатор обмеження реалізації продуктивного потенціалу озимої пшениці","综合评估2021—2025年乌克兰南部草原冬小麦（пшениця озима）生育期农业气象条件，并揭示NDVI、NDRE和NDMI植被指数在温度状况、降水和水分亏缺季节性变化影响下的动态规律。方法。研究在尼古拉耶夫国立农业大学教学-科学-实践中心基地进行。使用了Pessl Instruments自动气象站（iMETOS）2021—2025年的气象数据，以及经Google Earth Engine环境处理的Sentinel-2 SR Harmonized卫星数据。为评估作物状况，计算了NDVI、NDRE和NDMI指数，并结合温度状况、降水量及冬小麦主要物候发育阶段进程对其进行了比较分析。结果。确定了农业气象条件的显著年际差异，这些差异决定了植被指数的形成特征和季节性动态。2021—2022年和2022—2023年生育期对植株发育最为有利，NDVI值达到0.8—0.9，NDRE为0.6—0.7。2023—2024年生育期的特点是秋季发育受抑制，春季生物量补偿性增长，而2024—2025年良好的秋季生长条件未能保障春季的持续发育，原因是春季寒冷且相对干燥。确定水分胁迫发展的最关键时期为6月，此时植株从抽穗期过渡到籽粒灌浆期。NDMI指数对作物水分状况恶化反应最早，是水分胁迫发展最敏感的指标，而NDVI平台期持续时间的缩短则反映了作物生产潜力实现的受限。结论。冬小麦植被指数动态与温度状况和湿润条件的季节性进程特征密切相关。春季条件对植株生理状态的形成具有决定性意义，而就水分胁迫发展而言，从抽穗到籽粒灌浆的过渡期最为关键。NDMI指数宜作为水分胁迫的早期指标使用，而NDVI、NDRE和NDMI的综合应用可有效监测作物状况，NDVI平台期持续时间的缩短可作为冬小麦生产潜力实现受限的指标。","Аграрні інновації","2026-09-23T00:00:00Z",70,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":201},"基于五年气象站与Sentinel-2数据系统分析冬小麦植被指数动态，方法规范、结论明确，对遥感作物监测有参考价值，但属区域性研究，公共影响有限。",[203],{"name":197,"url":194},[205,28,206,207,30],"智慧农业","水分胁迫","冬小麦",[209,210],"乌克兰 南方草原 冬小麦 NDVI","Sentinel-2 NDMI 冬小麦 水分胁迫","乌克兰南方草原冬小麦NDVI-3483","10.32848\u002Fagrar.innov.2026.37.23",{"doi":212,"openalex_id":214,"authors":215,"venue":197,"cited_by_count":36,"oa_url":222,"card":223,"direction":52,"ingested_from":54},"W7214071557",[216,219],{"name":217,"orcid":218},"Антонина Панфилова","https:\u002F\u002Forcid.org\u002F0000-0003-0006-4090",{"name":220,"orcid":221},"Dmytro Koshkin","https:\u002F\u002Forcid.org\u002F0000-0002-6927-8487","https:\u002F\u002Fagrarian-innovations.izpr.ks.ua\u002Findex.php\u002Fagrarian\u002Farticle\u002Fdownload\u002F1168\u002F1166",{"tldr":224,"method":225,"finding":226,"direction":52,"opportunity":227},"评估乌克兰南部2021–2025年冬小麦生育期农业气象条件与NDVI、NDRE、NDMI植被指数动态","iMETOS自动气象站数据与Sentinel-2影像，在Google Earth","NDMI对水分胁迫响应最早最敏感，NDVI平台期缩短可指示产量潜力受限，抽穗至灌浆期为关键期。","可探索多指数融合的冬小麦水分胁迫早期预警阈值及平台期缩短与产量的定量模型。","2026-09-25T23:30:21.548558Z",{"id":230,"title":231,"url":232,"summary":233,"summary_zh":9,"content":9,"source_name":234,"source_url":232,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":239,"tags":241,"search_phrases":245,"slug":248,"view_count":36,"doi":249,"paper":250,"created_at":263},3180,"Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10690691\u002Fv1","Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis。Research Square","Research Square","2026-09-21T00:00:00Z",56,{"impact":21,"substance":69,"depth":70,"authority":57,"freshness":119,"relevant":22,"comment":238},"基于多时相NDVI\u002FNDWI的湿地植被与水文动态遥感评估，方法规范但属区域性案例研究，公共影响有限。",[240],{"name":234,"url":232},[27,242,28,243,244],"农业生态","湿地保护","NDWI",[246,247],"Sejnane 湿地 NDVI NDWI","突尼斯 拉姆萨尔湿地 遥感","Sejnane湿地NDVINDWI-3180","10.21203\u002Frs.3.rs-10690691\u002Fv1",{"doi":249,"openalex_id":251,"authors":252,"venue":234,"cited_by_count":36,"oa_url":232,"card":9,"direction":52,"ingested_from":54},"W7213955587",[253,255,257,260],{"name":254,"orcid":9},"Imen Khemiri",{"name":256,"orcid":9},"Chahida Chemingui",{"name":258,"orcid":259},"Alaeddine Rouissi","https:\u002F\u002Forcid.org\u002F0009-0000-9319-5182",{"name":261,"orcid":262},"Sahar Abidi","https:\u002F\u002Forcid.org\u002F0000-0003-3355-6876","2026-09-22T23:30:23.965045Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":276,"tags":278,"search_phrases":282,"slug":285,"view_count":36,"doi":286,"paper":287,"created_at":329},3083,"Multi-index analysis reveals complexity of tundra greening and shrubification on Yamal Peninsula","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2752-664x\u002Faea99e","Abstract Arctic vegetation cover is undergoing rapid structural and spatial change under a warming climate. Shrubification is a major component of these changes, whereby shrubs grow outwards and upwards, infilling existing patches and spreading into new areas. Satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), show increasing trends across decades in many regions of the Arctic, a phenomenon referred to as greening, and interpreted as an indicator of compositional, structural, spatial and functional changes in vegetation. However, the direct contribution of shrubification on satellite-level greening remains poorly understood, partly due to the spectral limitations of single-index methods and the coarse spatial resolution of multi-decadal satellite records. Here we examine and compare the spatial patterns and magnitude of greening on Yamal Peninsula, Arctic Russia, measured with four commonly used vegetation indices derived from Landsat imagery (1987-2023): NDVI, Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI), and kernel NDVI (kNDVI). We also examine how willow (Salix lanata) cover (%) relates to greening, using cover data derived from field observations, and Unoccupied Aerial Vehicle (UAV) and Very High Resolution WorldView 3 satellite imagery. Vegetation greened in 12–25% of the study area, driven primarily by vegetation regeneration on cryogenic landslides and possibly due to shrub expansion. However, greening magnitude was dependent on the VI selected, as they showed only a maximum of 54% similarity in spatial distribution. Uncertainty in the relationship between spectral greening and shrubification adds complexity to interpreting drivers of vegetation change, including how herbivory, for example by reindeer (Rangifer tarandusi), inhibits shrub expansion and how abiotic and other biotic influences may be detected by different or some combination of VIs. We demonstrate that decadal vegetation greenness changes have not been homogenous on Yamal Peninsula, with contrasting Salix-dominated areas responding differently to shared changes in climate, likely due to herbivore dynamics, soil effects, as well as microclimatic or topographical differences in the area.","摘要 在气候变暖背景下，北极植被覆盖正经历快速的结构和空间变化。灌木化是这些变化的主要组成部分，表现为灌木向外和向上生长，填充现有斑块并向新区域扩散。基于卫星的植被指数（VIs），如归一化差异植被指数（NDVI），在北极许多地区显示出数十年间的增长趋势，这一现象被称为绿化，并被解释为植被组成、结构、空间和功能变化的指标。然而，灌木化对卫星尺度绿化的直接贡献仍知之甚少，部分原因在于单一指数方法的光谱局限性以及多年代际卫星记录的空间分辨率较粗。本研究利用1987—2023年Landsat影像衍生的四种常用植被指数——NDVI、增强植被指数2（EVI2）、土壤调节植被指数（SAVI）和核NDVI（kNDVI）——考察并比较了俄罗斯北极亚马尔半岛绿化的空间格局和幅度。我们还利用野外观测、无人驾驶飞行器（UAV）和甚高分辨率WorldView 3卫星影像获得的覆盖度数据，研究了柳树（Salix lanata）覆盖度（%）与绿化的关系。研究区12%—25%的区域出现植被绿化，主要由低温滑坡上的植被再生驱动，也可能源于灌木扩张。然而，绿化幅度取决于所选用的植被指数，因为它们在空间分布上的相似性最高仅为54%。光谱绿化与灌木化之间关系的不确定性增加了解释植被变化驱动因素的复杂性，包括植食作用（例如驯鹿Rangifer tarandus的啃食）如何抑制灌木扩张，以及非生物和其他生物影响如何通过不同植被指数或其组合被检测到。我们证明，亚马尔半岛数十年尺度的植被绿度变化并非均一，以柳树为主的对比区域对共同的气候变化响应不同，这可能归因于植食动物动态、土壤效应以及该地区微气候或地形的差异。","Environmental Research Ecology","2026-09-18T00:00:00Z",69,{"impact":21,"substance":274,"depth":69,"authority":20,"freshness":21,"relevant":22,"comment":275},22,"多指数遥感揭示北极苔原绿化与灌木扩张复杂性，方法新颖数据扎实，但属基础生态研究，与三农信息化关联间接，公共价值有限。",[277],{"name":270,"url":267},[279,28,30,280,281],"气候变化","北极苔原","灌木扩张",[283,284],"北极苔原 灌木扩张 遥感","北极苔原 植被指数 气候变化 灌木扩张","北极苔原灌木扩张遥感-3083","10.1088\u002F2752-664x\u002Faea99e",{"doi":286,"openalex_id":288,"authors":289,"venue":270,"cited_by_count":36,"oa_url":323,"card":324,"direction":52,"ingested_from":54},"W7213598351",[290,293,296,298,300,303,306,309,312,315,317,320],{"name":291,"orcid":292},"Elias Koivisto","https:\u002F\u002Forcid.org\u002F0009-0007-6204-0963",{"name":294,"orcid":295},"Anton Kuzmin","https:\u002F\u002Forcid.org\u002F0000-0001-5066-5535",{"name":297,"orcid":9},"Logan Berner",{"name":299,"orcid":9},"Jeff T Kerby",{"name":301,"orcid":302},"Mariana Verdonen","https:\u002F\u002Forcid.org\u002F0000-0001-9780-0052",{"name":304,"orcid":305},"Anna Skarin","https:\u002F\u002Forcid.org\u002F0000-0003-3221-1024",{"name":307,"orcid":308},"Tiina H. M. Kolari","https:\u002F\u002Forcid.org\u002F0000-0003-0955-2402",{"name":310,"orcid":311},"Teemu Tahvanainen","https:\u002F\u002Forcid.org\u002F0000-0002-7856-299X",{"name":313,"orcid":314},"Pasi Korpelainen","https:\u002F\u002Forcid.org\u002F0009-0005-9956-6016",{"name":316,"orcid":9},"Miguel Villosada",{"name":318,"orcid":319},"Bruce C. Forbes","https:\u002F\u002Forcid.org\u002F0000-0002-4593-5083",{"name":321,"orcid":322},"Timo Kumpula","https:\u002F\u002Forcid.org\u002F0000-0002-2716-7420","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2752-664X\u002Faea99e\u002Fpdf",{"tldr":325,"method":326,"finding":327,"direction":52,"opportunity":328},"用四种植被指数分析亚马尔半岛苔原绿化，揭示灌木扩张与光谱绿化的复杂关系。","Landsat 1987-2023年NDVI、EVI2、SAVI、kNDVI及无","绿化面积12-25%，但不同指数空间分布相似度仅54%，灌木扩张与绿化关系不确定。","多指数遥感可揭示植被变化的异质性，需结合地面与高分辨率数据解析生物与非生物驱动机制。","2026-09-21T23:30:27.685080Z"]