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为了验证Cryosat-2/SIRAL数据监测湖泊水位的能力,提高其提取湖泊水位变化的精度,以青海湖为研究对象,利用主波峰重心偏移法、主波峰阈值法、主波峰5-β参数法、传统重心偏移法、传统阈值法和传统5-β参数法6种算法对Cryosat-2/SIRAL LRM 1级数据进行波形重跟踪,提取青海湖2010—2015年湖泊水位,对比不同算法获取水位的精度,并结合Envisat/RA-2 GDR数据,延长水位变化时间序列,获得青海湖2002年—2015年的水位变化信息。结果表明,主波峰5-β参数法提取湖泊水位的精度最好,均方根误差为0.093 m;对于GDR产品中LRM模式的3种数据,基于Refined OCOG算法的数据更适合湖泊水位的提取;青海湖2002年—2015年水位整体上涨,水位平均变化趋势为0.112 m/年,年内水位变化呈现明显的季节性。
In order to verify the ability of Cryosat-2 / SIRAL data to monitor lake water level and improve the precision of lake water level extraction, taking Qinghai Lake as the research object, the main peak center-of-gravity shift method, main peak threshold method and main peak 5-β parameter method , Traditional centroid migration method, traditional threshold method and traditional 5-βparameter method, the waveform of the Cryosat-2 / SIRAL LRM level 1 data is tracked again. The lake water level of Qinghai Lake from 2010 to 2015 is extracted and compared with the different algorithms to obtain the water level , And combined with Envisat / RA-2 GDR data, extend the time series of water level change to obtain the information of water level change of Qinghai Lake from 2002 to 2015. The results showed that the accuracy of the main peak 5-β parameter method for extracting lake water level was the best, with a root mean square error of 0.093 m. For the three data of LRM model in GDR products, the data based on Refined OCOG algorithm was more suitable for the extraction of water level in lakes; Qinghai Lake From 2002 to 2015, the water level as a whole rose, the average water level change trend was 0.112 m / year, and the water level showed obvious seasonal changes during the year.