Sentinel Data(哨兵数据)研究综述
Sentinel Data 哨兵数据 - METHODS Using a retrospective new-user cohort design, we identified patients with a diagnosis code for depression aged ≥12 years who were continuously enrolled in a Sentinel Data Partner health plan for ≥180 days before their first sertraline dispensing between June 30, 2006 and September 30, 2015. [1] The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. [2] The various vegetation indices (VIs) were derived from Landsat and Sentinel data. [3] Fusion of Sentinel datasets provide a reliable means of impervious surface mapping at city scale as an indicator of environmental quality which is valuable for the sustainable management of the city. [4] However, owing to the 10-m spatial resolution of Sentinel data, previous studies mostly focused on the mapping of large waterbodies. [5] Thus, sentinel data coupled with advanced image processing technique like ICA could identify maximum information about the boundaries and micro landform within the coastal sand dune complex in a cost-effective manner. [6] Differences in the preferred propagation direction of the recorded wave structures from the KEO Sentinel data from the directions obtained with photometers at the same observation point [Tashchilin, 2010, Podlesny, 2018], probably, associated with different observation heights. [7] Methods We conducted a meta-analysis of data extracted from studies published between 2004 and 2017 and from sentinel data from the European surveillance system (TESSy) between 2004 and 2018. [8] The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models. [9] The Gram–Schmidt (GS) fusion process was chosen to combine the multispectral Sentinel data and VH, VV bands of Sentinel-1 data with different window size. [10] Study design Sentinel data obtained for influenza virus, respiratory syncytial virus, human metapneumovirus and rhinovirus cases were analyzed and compared between the season 2019/ 2020 and the five previous seasons. [11] The advent of the cloud-based platform of Google Earth Engine (GEE) has allowed the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. [12] The impact on the correlation between modeled and in situ soil moisture measurements was similar when assimilating the Sentinel data using WCM (Δ R = +0. [13] This article first popularizes the domestic urban environmental conditions and knowledge of remote sensing technology, and then analyzes the process of obtaining urban greenness sentinel data and Landsat data of coastal cities from remote sensing data, and finally analyzes the urban greenness of coastal cities in the past 5 years. [14] We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. [15] All indicators are based on Sentinel data and derived by capitalizing on EO Platforms and advanced Artificial Intelligence as a key enabler and accelerator of information discovery. [16] Terrascope is a processing platform that holds Sentinel data and derived products, so they can be used for further analysis directly. [17] Results showed that BRR modelling using publicly available Sentinel data with the addition of local electromagnetic induction surveys or gamma radiometric surveys produced the best forecasts as determined by the classical performance metrics. [18] The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. [19] However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. [20] These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy. [21] In this study, we develop a classification system that consistently produces accurate local climate zone (LCZ) maps at intra-urban scale for 40 cities using Sentinel data. [22] Overall, the findings of this study show that an estimated length of a river inundated by water can be determined using new-generation Sentinel data and these results provide new insights on the dynamics of N-PRs – a previously challenging task with broadband multispectral satellite datasets. [23] The proposed methodology is applied to the Los Angeles highway and freeway network, using Sentinel data from 2016 to 2019. [24] It was found that: (1) The synergistic use of Sentinel-2 MSI and Sentinel-1 SAR data can improve the accuracy of LCZ classification; (2) The multi-seasonal information of Sentinel data also has a good contribution to LCZ classification; (3) OSM, GLCM, SI, and NTL datasets have some positive contribution to LCZ classification when individually adding them to the seasonal Sentinel-1 and Sentinel-2 datasets; (4) It is not an absolute right way to improve LCZ classification accuracy by combining as many datasets as possible. [25] However, most floating debris is difficult to observe by the above platform because it often has a smaller size than the highest spatial resolution of the Sentinel data. [26] Records for ill travellers with at least one confirmed or probable diagnosis, were extracted from the GeoSentinel database for the CIWEC Clinic Kathmandu site from January 1, 2009 to December 31, 2017. [27] In Europe, the Copernicus Program offers numerous territorial monitoring tools to users and decision makers, such as Sentinel data. [28] The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. [29] Detection rates for different viruses were compared to 2017-2019 sentinel data (15350 samples; week 1-38, 11823 samples). [30] The watershed has been delineated into nine sub-watersheds and hydrogeomorphology, drainage, drainage density, slope, NDVI and NDWI of the study area has been carried out using Landsat data 2010 and Sentinel data 2020 in ARCGIS 10. [31] 82 per 1000 patients for sentinel data. [32] In this study, a systematic method for spatial and temporal phenological estimation was developed based on multi-temporal Sentinel data. [33] Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. [34] Moreover, because of the longer time series of Landsat data than Sentinel data, the long-term variation trend of sea ice in fixed areas can be monitored. [35] Based on the Landsat and Sentinel data from 2005 to 2019, this paper uses NEVI to monitor the vegetation destruction and restoration of the Shengli mining area. [36] We utilised the Oxford Royal College of General Practitioners Research and Surveillance Centre sentinel database to examine English patients who received vaccination between 2014/2015 and 2018/2019. [37] Hence, in case of regional analysis and high cost of Radarsat-2 dataset, one can use freely available sentinel data for RVI analysis due to its wider swath coverage. [38] The map was generated from the analysis of Sentinel data, orthomosaics and 3D optical models generated by the application of SfM techniques to UAV images, as well as bathymetry and backscatter intensity measurements. [39] The current study tested an innovative approach like Time-Weighted Dynamic Time Warping (TWDTW) in a machine learning environment with time-series Sentinel data to map the various crop types. [40]方法 使用回顾性新用户队列设计，我们确定了诊断代码为 ≥12 岁的抑郁症患者，这些患者在 2006 年 6 月 30 日至 9 月 30 日之间首次使用舍曲林之前，连续参加 Sentinel Data Partner 健康计划 ≥180 天, 2015 年。 [1] 免费软件云计算服务的出现极大地改进了景观研究，从而可以快速探索和处理卫星图像，例如 Landsat 和 Copernicus Sentinel 数据集。 [2] 各种植被指数 (VI) 来自 Landsat 和 Sentinel 数据。 [3] Sentinel 数据集的融合提供了一种可靠的城市尺度不透水地表测绘方法，作为环境质量的指标，这对于城市的可持续管理很有价值。 [4] 然而，由于 Sentinel 数据的空间分辨率为 10 米，以前的研究主要集中在大型水体的测绘上。 [5] 因此，哨点数据与 ICA 等先进的图像处理技术相结合，可以以具有成本效益的方式最大程度地识别沿海沙丘复合体内的边界和微地貌信息。 [6] 记录的波结构的首选传播方向与 KEO Sentinel 数据与在同一观测点用光度计获得的方向的差异 [Tashchilin, 2010, Podlesny, 2018]，可能与不同的观测高度有关。 [7] 方法 我们对从 2004 年至 2017 年发表的研究以及从 2004 年至 2018 年欧洲监测系统 (TESSy) 的哨兵数据中提取的数据进行了荟萃分析。 [8] 使用 Sentinel 数据在红树林中进行的 AGB 估计研究应该更多地关注使用深度学习等机器学习算法，而不是半经验模型。 [9] 选择 Gram-Schmidt (GS) 融合过程来结合不同窗口大小的 Sentinel-1 数据的多光谱 Sentinel 数据和 VH、VV 波段。 [10] 学习规划 对获得的流感病毒、呼吸道合胞病毒、人偏肺病毒和鼻病毒病例的哨点数据进行了分析和比较，并在 2019/2020 季节与前五个季节之间进行了比较。 [11] 谷歌地球引擎 (GEE) 基于云的平台的出现允许快速探索和处理卫星图像，例如 Landsat 和 Copernicus Sentinel 数据集。 [12] 当使用 WCM 同化 Sentinel 数据时，模拟和原位土壤水分测量之间的相关性的影响是相似的 (ΔR = +0. [13] 本文首先普及了国内城市环境状况和遥感技术知识，然后分析了从遥感数据中获取沿海城市城市绿度哨点数据和Landsat数据的过程，最后分析了以往沿海城市的城市绿度。 5年。 [14] 我们使用集成卡尔曼滤波器数据同化将来自多源 Sentinel 数据的叶面积指数 (LAI) 和土壤水分与 CERES-Wheat 模型相结合。 [15] 所有指标均基于 Sentinel 数据，并通过利用 EO 平台和高级人工智能作为信息发现的关键推动者和加速器而得出。 [16] Terrascope 是一个处理平台，包含 Sentinel 数据和衍生产品，因此可以直接用于进一步分析。 [17] 结果表明，使用公开可用的 Sentinel 数据以及添加局部电磁感应测量或伽马辐射测量的 BRR 建模产生了由经典性能指标确定的最佳预测。 [18] 结果如下： (1) 最优光谱指数(2D, 3D)可以有效地考虑相互作用效应和响应土壤性质敏感波段之间波段的可能组合，规避光谱指数在不同区域的适用性问题; (2) Landsat-8 OLI 和 Sentinel-2A MSI 多光谱 RS 数据源均经过一阶导数技术处理后，模型的预测精度有所提高； (3) 预测模型的最佳性能/准确性是针对一阶导数下的哨点数据。 [19] 然而，如果派生的离散变量不能区分广泛的植被类型，哨兵数据可能会受限于理解精确的栖息地-物种关联。 [20] 这些结果表明 SMAP/Sentinel 数据在改进区域尺度 SM 估计和创建具有更高准确性的 LSM 的进一步应用方面的潜在用途。 [21] 在这项研究中，我们开发了一个分类系统，该系统使用 Sentinel 数据始终如一地为 40 个城市生成准确的城市内气候区 (LCZ) 地图。 [22] 总体而言，这项研究的结果表明，可以使用新一代 Sentinel 数据确定被水淹没的河流的估计长度，这些结果为 N-PR 的动力学提供了新的见解——这是宽带多光谱卫星数据集之前的一项具有挑战性的任务. [23] 使用 2016 年至 2019 年的 Sentinel 数据，将所提出的方法应用于洛杉矶高速公路和高速公路网络。 [24] 发现：（1）Sentinel-2 MSI和Sentinel-1 SAR数据的协同使用可以提高LCZ分类的准确性； (2) Sentinel 数据的多季节信息对 LCZ 分类也有很好的贡献； (3) OSM、GLCM、SI 和 NTL 数据集在将它们单独添加到季节性 Sentinel-1 和 Sentinel-2 数据集时，对 LCZ 分类有一些积极贡献； (4) 尽可能多地组合数据集来提高 LCZ 分类精度并不是绝对正确的方法。 [25] 然而，大多数漂浮碎片很难通过上述平台观察到，因为它的尺寸通常小于 Sentinel 数据的最高空间分辨率。 [26] 从 2009 年 1 月 1 日至 2017 年 12 月 31 日期间，从 CIWEC 诊所加德满都站点的 GeoSentinel 数据库中提取了至少有一项确诊或可能诊断的患病旅行者的记录。 [27] 在欧洲，哥白尼计划为用户和决策者提供了许多领土监测工具，例如 Sentinel 数据。 [28] Sentinel 数据对于 LCLU 分类和森林生物量估计的研究具有巨大潜力。 [29] 将不同病毒的检测率与 2017-2019 年的前哨数据（15350 个样本；第 1-38 周，11823 个样本）进行了比较。 [30] 这 流域被划分为九个子流域和水文地貌， 研究区排水量、排水密度、坡度、NDVI、NDWI 使用 ARCGIS 10 中的 Landsat 数据 2010 和 Sentinel 数据 2020 进行。 [31] 每 1000 名患者中有 82 名用于前哨数据。 [32] 在本研究中，基于多时相 Sentinel 数据开发了一种系统的时空物候估计方法。 [33] 在此基础上，结合提出的问题，本研究选取干旱区艾比湖盆地的典型区域作为研究区，以哨兵数据为主要数据源，利用哨兵一号A（雷达数据） 、Sentinel-2A和Sentinel-3A（多光谱数据），结合16种DEM导数和气候数据（年平均气温MAT、年平均降水量MAP）作为分析。 [34] 此外，由于 Landsat 数据的时间序列比 Sentinel 数据更长，因此可以监测固定区域海冰的长期变化趋势。 [35] 本文基于 2005 年至 2019 年 Landsat 和 Sentinel 数据，利用 NEVI 监测胜利矿区植被破坏和恢复情况。 [36] 我们利用牛津皇家全科医师学院研究和监测中心哨兵数据库来检查 2014/2015 年至 2018/2019 年期间接受疫苗接种的英国患者。 [37] 因此，在 Radarsat-2 数据集的区域分析和高成本的情况下，由于其覆盖范围更广，可以使用免费提供的哨点数据进行 RVI 分析。 [38] 该地图是通过分析 Sentinel 数据、正射镶嵌和 3D 光学模型生成的，这些模型是通过将 SfM 技术应用于无人机图像以及测深和反向散射强度测量而生成的。 [39] 目前的研究在机器学习环境中测试了一种创新方法，如时间加权动态时间规整 (TWDTW)，并使用时间序列 Sentinel 数据来映射各种作物类型。 [40]
Copernicu Sentinel Data
The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. [1] The advent of the cloud-based platform of Google Earth Engine (GEE) has allowed the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. [2]免费软件云计算服务的出现极大地改进了景观研究，从而可以快速探索和处理卫星图像，例如 Landsat 和 Copernicus Sentinel 数据集。 [1] 谷歌地球引擎 (GEE) 基于云的平台的出现允许快速探索和处理卫星图像，例如 Landsat 和 Copernicus Sentinel 数据集。 [2]
Available Sentinel Data
Results showed that BRR modelling using publicly available Sentinel data with the addition of local electromagnetic induction surveys or gamma radiometric surveys produced the best forecasts as determined by the classical performance metrics. [1] Hence, in case of regional analysis and high cost of Radarsat-2 dataset, one can use freely available sentinel data for RVI analysis due to its wider swath coverage. [2]结果表明，使用公开可用的 Sentinel 数据以及添加局部电磁感应测量或伽马辐射测量的 BRR 建模产生了由经典性能指标确定的最佳预测。 [1] 因此，在 Radarsat-2 数据集的区域分析和高成本的情况下，由于其覆盖范围更广，可以使用免费提供的哨点数据进行 RVI 分析。 [2]