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首页> 外文会议>Sensors, systems, and next-generation satellites XIV >EVENT DETECTION OF HYDROLOGICAL PROCESSES WITH PASSIVE L-BAND DATA FROM SMOS
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EVENT DETECTION OF HYDROLOGICAL PROCESSES WITH PASSIVE L-BAND DATA FROM SMOS

机译：利用SMOS的被动L波段数据进行水文过程的事件检测

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Since it's launch, the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite, is delivering new data from its L-Band 1.4Ghz 2D interferometer [1]. The observations from SMOS are used to retrieve soil moisture in the first centimeters and ocean salinity at the surface of the water. The observations are multi-angular with a 3 days maximum revisit time. The spatial resolution of SMOS data is 40km.rnIn this paper we present on event detection algorithm implemented at CATDS (Centre Aval de Traitement des Donnees SMOS) the CNES level 3 and level 4 SMOS enter. This algorithm is a three stage change detection algorithm. At stage one the possibility/probability of occurrence of the event is evaluated. This is done via spatio-temporal constraints maps. These maps are obtained from the analysis of NSIDC's freezing index products over the last century. Climate data from ancillary files are tested will taking into consideration the uncertainty of the data. Some selected retrieved variables are also tested. At stage two a time series analysis is applied. In the current version of the algorithm a direct change detection algorithm is used. The tests make use of available variables of polarization index, retrieved soil moisture...Finally at stage three a simple fuzzy logic approach is used to decide if the event occurred. This approaches takes into consideration the separation time of the data. Ascending and descending orbits are taken into consideration. In this study freezing detection is presented over central CONUS. The temporal and angular signature of SMOS will be presented. Comparison is done with the SCAN network
机译：自发射以来，ESA的土壤湿度和海洋盐度（SMOS）卫星正在从其L波段1.4Ghz二维干涉仪[1]提供新数据。来自SMOS的观测数据被用于获取第一厘米的土壤湿度和水面的盐度。观察结果是多角度的，最长重访时间为3天。 SMOS数据的空间分辨率为40 km。在本文中，我们介绍了在CATDS（Centre Aval de Traitement des Donnees SMOS）上实现的事件检测算法，CNES级别3和级别4进入了SMOS。该算法是三阶段变化检测算法。在第一阶段，评估事件发生的可能性/可能性。这是通过时空约束图完成的。这些图是通过对NSIDC上个世纪的冰冻指数产品的分析获得的。测试来自辅助文件的气候数据时将考虑数据的不确定性。还测试了一些选定的检索变量。在第二阶段，应用时间序列分析。在该算法的当前版本中，使用直接变化检测算法。测试利用极化指数，获取的土壤水分的可用变量...最后，在第三阶段，使用简单的模糊逻辑方法确定事件是否发生。该方法考虑了数据的分离时间。考虑到上升和下降轨道。在这项研究中，通过中央CONUS进行了冷冻检测。将介绍SMOS的时间和角度特征。通过SCAN网络进行比较

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