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1.
????????????δ?????????????????????????????????·?????????????????????????????????????????????????????????÷?Χ???ó?????:????γ??????Χ??С??8???,??x??y???????????????1 cm??????????侫???????γ??(?????γ??)??Χ????С??????????  相似文献   

2.
????????????????????????????????Χ????????????????????????????????????????????????????????????4??IGSO????????????????????????Χ???γ55??????????γ75??  相似文献   

3.
????TEQC????????GPS????????????????????????????????????м????????????????MP1??MP2??CSR?????????????????IGS??????????????????飬?ó???1????????????????????????з??????仯,???????????仯?????,??????????????????????????????????????Χ?????????????Χ?????????￡?2?????????????????????????????????????????????????????????????и???????????????3??“????”?????30????????90%????IGS????????????????????????????Χ??????????????  相似文献   

4.
????????????????????????????,??????й???????????????????????Χ?????????????????????????????????????????????????????????????п????????????????????????????????????й???????????????Χ??  相似文献   

5.
?????????????κ?InSAR????????????????????????????????????PSGRN/PSCMP???????????????????????Ms7.1???????????????????α????????????1)???????????????????????????????????????λ??仯????????????????α????????2)????????????????仯??????????????????λ?????Χ???????????????仯???Χ??3)???????????????￡?????????????????????????????仯?????????????????????????????????????????????仯?????????????????????????仯?????????????????????仯??  相似文献   

6.
???????????1990??1997??1997??2003??1994??2006??????????????????????λ??????о???Ms8.0???????????Ms6.1?????????????????????????????????????????????????????????????????????????μ???????????????????????????????????????μ??Χ??????????????????10???????????????????????????????????????????????????????1997??2003?????????????????????????????????????????  相似文献   

7.
????GPS?????????????????????????????????????????????????????????????????GPS????????????????????Χ????λ??????к??????????????????????????????????????????????????????????????ε?????????????ó????3???????????????????????????η?Χ????????????????λ???仯???????????????????Χ????????????????飬???????????????????С???????????λ????е??????????????????????????ο????????????????????????????????????????ο??嶫????γ????????η?????????????硪??????????????γ????????????????????????ο???????????ο??嶫????????????????й????????????  相似文献   

8.
??????????10????GPS??λ???200??2003??2007????SAR?????????InSAR???????????????????????????????????????????????????????????????????????????????????????????????????????????????磬????????????????磬???????????????Χ???????????????α??????3????????1 mm/a, ???????????????Χ????????????1 mm/a,??????????????????С??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????,?????????????????1~-1.5 mm/a???????????????п??????????壬???????????????????????????????????????α???????????????????????????????????????????????????  相似文献   

9.
??????????????Χ??GPS??????????????з?????????t???????λ?????????α?????50 km??Χ????GPS????α???????????????????70 mm????????????30 s???????λ??????????????????α????????λ???????????У???С?????о???????????????29 km????????λ???21 mm????????150 km???λ???1??2 mm????GPS???????α????????????????????????????????????????????λ?????и???20 km????????廬?????0.6 m??GPS????????α??о????????????????????????????????ε?????????????????  相似文献   

10.
???????D??InSAR?????????????????ξ???仯????????????α??????BM3????Χ?????編?????????????????????????????????α???δ????????????????????????????????????α????????????仯??????????С??Χ????Σ???InSAR?α????????????BM3??????????????????????????λ??????編????????????3?????????????????????????????????5.11 mm????????????BM4??????????4???????????????仯??????仯??BM3???????????????￡????????·???????????????????????????????????????????????????????????????????????  相似文献   

11.
????????????????????????????С??????????????????????????????????????????????????η??????????????????????????????ε???Ч?????????????庣???T/P???????????????Beta-5?????????????????????????÷????????????????????????????κ???????K???  相似文献   

12.
湖泊水位是评估湖泊水量变化的重要指标。本文以洪泽湖、高邮湖及洞庭湖为研究对象,利用集中度的概率密度函数方法(CPDF)来提高Jason-2测高数据精度,分析了降水量与各个湖泊水位变化的相关性,并基于实测水位数据对比评价了Jason-2测高卫星原始GDR数据和CPDF方法处理后的卫星数据的精度。结果表明:①Jason-2原始GDR数据点的分布存在疏密之分,大部分数据分布相对集中,且有一定的周期变化,但评价结果显示精度较差,故原始GDR数据不能直接用于湖泊水位监测;②CPDF方法可以极大提高测高卫星的水位数据精度,洪泽湖与高邮湖的均方根误差分别由1.92 m与1.74 m减少到了0.32 m和0.36 m,相关系数由0.28和0.04提高到了0.85和0.72。对于南北宽度较窄且日水位变化较大的湖泊(如洞庭湖),CPDF方法提高原始GDR结果的精度有限;③洞庭湖降水与水位相关性最强,高邮湖次之,而洪泽湖降水与水位成不显著的负相关,是洪泽湖水利工程对于水位的调节导致了这一结果。本研究对于利用测高卫星获得湖泊水位值,进而对湖泊进行动态监控,特别是在填补资料匮乏地区湖泊水位数据方面具有重要意义。  相似文献   

13.
对两颗太阳同步轨道测高卫星ERS-2和Envisat 1995～2010年169个重复周期（共近15 a）的数据进行沿轨正交响应分析，得到中国海及西太平洋地区的海洋潮汐调和常数，并利用交叉点位置上升轨迹与下降轨迹提取的潮汐信息和验潮站数据分析了结果的准确度。结果表明，除S2、K1分潮外，基于ERS-2和Envisat测高卫星能够提取出可靠和分布合理的潮汐调和常数，结合交叉轨迹与邻近地面轨迹将有效改善采样规律和观测样本数量，进一步提高潮汐参数的准确度。  相似文献   

14.
��TOPEX/Poseidon���鳱վ�����ۺ�ƽ��仯   总被引：1，自引：0，他引：1  
????TOPEX/Poseidon????????????鳱??????????1994~2000??????????????????????????仯???????????????????????????仯??????к?????????????T/P??????????????????????????????7.73 mm/a???????鳱?????????12.18 mm/a????????????????4.45 mm/a?????????????鳱?????????????????????????????????鳱???????????鳱???????????????????????????????鳱?????????????????  相似文献   

15.
Lake monitoring by remote sensing is of significant importance to understanding the lake and ambient ecological and environmental processes. In particular, whether lake water storage variation could predict lake surfacial temperature or vice versa has long fascinated the research community, in that it would greatly benefit the monitoring missions and scientific interpretation of the lake change processes. This study attempted to remotely detect the dynamics of the Aral Sea and pursue the relationships between varying lake water storage attributes and surface water temperature by using MODIS LST(Moderate-resolution Imaging Spectroradiometer Land Surface Temperature) 8-day composite products, satellite altimeter data, and actual meteorological measurements. Their associations with lake Surface Water Temperatures(SWT) were then analyzed. Results showed the lake water surface areas and elevations of the North Aral Sea tended to increasing trend from 2001(2793.0 km~2, 13.6 m) to 2015(6997.8 km~2, 15.9 m), while those of the South Aral Sea showed a decreasing trend during 2001(20 434.6 km~2, 3.9 m) and 2015(3256.1 km~2, 0.9 m). In addition, the annual daytime and nighttime lake SWT both decreased in the North Aral Sea, while only the daytime SWT in the South Aral Sea exhibited an increase, indicating a rising deviation of diurnal temperatures in the South Aral Sea during the past 15 yr. Moreover, a lower correlation was found between variations in the daytime SWT and storage capacity in the South Aral Sea(R~2 = 0.33; P 0.05), no fair correlations were tested between lake water storage and daytime SWT in the North Aral Sea nor between lake water storage and nighttime SWT in either part of the sea. These results implied that climate change, if any at least during the research period, has no significant effects on lake dynamics over the two sectors of the Aral Sea with anthropogenic disturbances. However, climate change and human activities may overlap to explain complex consequences in the lake storage variations. Our results may provide a reference for monitoring the spatiotemporal variations of lakes, increasing understanding of the lake water storage changes in relation to the lake SWT, which may benefit the ecological management of the Aral Sea region, in the effort to face the likely threats from climate change and human activities to the region.  相似文献   

16.
For validating the results of retrieved mean wave period, four empirical algorithms established previously are introduced. Based on the data of over five years derived from TOPEX satellite altimeter for the entire East China Sea, ocean wave periods were calculated and statistical comparison among them was performed. The retrieved mean wave period obtained with our new distribution parameters showed better agreement with the wave period TB measured by buoy than that calculated by other three algorithms. The difference between the mean values of and that of TB is 0.16 s and the RMSE (root mean square error) of is the lowest value (0.48).  相似文献   

17.
选取2013~2015年南水北调中线工程核心水源区数字地震台网记录的141次ML≥1.5地震的数字地震资料,采用Atkinson方法计算核心水源区地震波Q值。结果表明,该地区非弹性衰减结果为Q=146.0×f(0.770 3),处于中等Q值的构造活跃期。  相似文献   

18.
????JASON-1??????????????????????????????????????????????????????????????????????????????????????????-5~5 mm???????????????10 cm????????????????????????????????????????????????????(???????????????????)??  相似文献   

19.
????2004??2008???ICESat???????????????GLA12???????????????????????????????????????仯????????????????????????????????????仯??????????????????????????????3 m??????????????????????仯?????????????????????????????????  相似文献   

20.
利用Jason-1 SGDR数据分析Tseng波形修正法存在的问题,通过修改参考波形选取策略和异常点插值的定权策略提出改进的波形修正算法。实验表明,改进算法能够避免Tseng法导致的波形前缘变形问题,提高近海海面高的精度。  相似文献   

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