Validation of Jason and Envisat Altimeter Dual Frequency Rain Flags

New rain flags based on the dual frequency capabilities of the new Jason Poseidon-2 and Envisat RA2 altimeters have been tested, developed and adopted for the operational processing of the altimeter data. Their validation conducted during the calibration/validation phases of the satellites is presented here. The Jason flag is validated by comparison with the TOPEX one, using the Tandem mission. The results show a very good agreement between the two sensors and the two rain flags The Envisat flag is validated by comparison with both Jason and TOPEX using global and collocated data sets. The results show similar performances for the three sensors. The f relations estimated during the calibration-validation period and presented here have been given to the altimeter ground processing facilities for operational use.     

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卫星测高数据监测青海湖水位变化

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。     

一种基于Envisat ASAR波模式数据的有效波高反演算法

The main objective of this paper is to propose a newly developed ocean Significant Wave Height(SWH) retrieval method from Envisat Advanced Synthetic Aperture Radar(ASAR) imagery. A series of wave mode imagery from January, April and May of 2011 are collocated with ERA-Interim reanalysis SWH data. Based on the matched datasets, a simplified empirical relationship between 22 types of SAR imagery parameters and SWH products is developed with the Genetic Algorithms Partial Least-Squares(GA-PLS) model. Two major features of the backscattering coefficient σ_0 and the frequency parameter S_(10) are chosen as the optimal training feature subset of SWH retrieval by using cross validation. In addition, we also present a comparison of the retrieval results of the simplified empirical relationship with the collocated ERA-Interim data. The results show that the assessment index of the correlation coefficient, the bias, the root-mean-square error of cross validation(RMSECV) and the scattering index(SI) are 0.78, 0.07 m, 0.76 m and 0.5, respectively. In addition, the comparison of the retrieved SWH data between our simplifying model and the Jason-2 radar altimeter data is proposed in our study.Moreover, we also make a comparison of the retrieval of SWH data between our developed model and the wellknown CWAVE_ENV model. The results show that satisfying retrieval results are acquired in the low-moderate sea state, but major bias appears in the high sea state, especially for SWH5 m.     

基于星载激光雷达和雷达高度计数据的南极冰盖表面高程制图

南极数字高程模型DEMs(Digital Elevation Models)是研究极区大气环流模式,南极冰盖动态变化和南极科学考察非常重要的基础数据。目前,科学家已经发布了五种不同的南极数字表面高程模型。这些数据都是由卫星雷达高度计,激光雷达和部分地面实测数据等制作而成。尽管如此,由于海洋与冰盖交接的南极冰盖边缘区随时间的快速变化,有必要根据新的卫星数据及时更新南极冰盖表面高程数据。因此,我们利用雷达高度计数据(Envisat RA-2)和激光雷达数据(ICESat/GLAS)制作了最新的南极冰盖高程数据。为提高ICESat/GLAS数据的精度,本文采用了五种不同的质量控制指标对GLAS数据进行处理,滤除了8.36%的不合格数据。这五种质量控制指标分别针对卫星定位误差、大气前向散射、饱和度及云的影响。同时,对Envisat RA-2数据进行干湿对流层纠正、电离层纠正、固体潮汐纠正和极潮纠正。针对两种不同的测高数据,提出了一种基于Envisat RA-2和GLAS数据光斑脚印几何相交的高程相对纠正方法,即通过分析GLAS脚印点与Envisat RA-2数据中心点重叠的点对,建立这些相交点对的高度差(GLAS-RA-2)与表征地形起伏的粗糙度之间的相关关系,对具有稳定相关关系的点对进行Envisat RA-2数据的相对纠正。通过分析南极冰盖不同区域的测高点密度,确定最终DEM的分辨率为1000 m。考虑到南极普里兹湾和内陆地区的差异性,将南极冰盖分为16个区,利用半方差分析确定最佳插值模型和参数,采用克吕金插值方法生成了1000 m分辨率的南极冰盖高程数据。利用两种机载激光雷达数据和我国多次南极科考实测的GPS数据对新的南极DEM进行了验证。结果显示,新的DEM与实测数据的差值范围为3.21—27.84 m,其误差分布与坡度密切关系。与国际上发布的南极DEM数据相比,新的DEM在坡度较大地区和快速变化的冰盖边缘地区精度有较大改进。     

Validation of Jason and Envisat Altimeter Dual Frequency Rain Flags

New rain flags based on the dual frequency capabilities of the new Jason Poseidon-2 and Envisat RA2 altimeters have been tested, developed and adopted for the operational processing of the altimeter data. Their validation conducted during the calibration/validation phases of the satellites is presented here. The Jason flag is validated by comparison with the TOPEX one, using the Tandem mission. The results show a very good agreement between the two sensors and the two rain flags The Envisat flag is validated by comparison with both Jason and TOPEX using global and collocated data sets. The results show similar performances for the three sensors. The f relations estimated during the calibration-validation period and presented here have been given to the altimeter ground processing facilities for operational use.     

Modelling the current and future spatial distribution of NPP in a Mediterranean watershed

The aim of this study is to use full spatial resolution Envisat MERIS data to drive an ecosystem productivity model for pine forests along the Mediterranean coast of Turkey. The Carnegie, Ames, Stanford Approach (CASA) terrestrial biogeochemical model, designed to simulate the terrestrial carbon cycle using satellite sensor and meteorological data, was used to estimate annual regional fluxes in terrestrial net primary productivity (NPP). At its core this model is based on light-use efficiency, influenced by temperature, rainfall and solar radiation. Present climate data was generated from 50 climate stations within the watershed using co-kriging. Regional scale pseudo-warming data for year 2070 were derived using a Regional Climate Model (RCM) these data were used to downscale the GCM General Circulation Model for the research area as part of an international research project called Impact of Climate Changes on Agricultural Production Systems in Arid Areas (ICCAP). Outputs of climate data can be moderated using the four variables of percent tree cover, land cover, soil texture and NDVI. This study employed 47 MERIS images recorded between March 2003 and September 2005 to derive percent tree cover, land cover and NDVI. Envisat MERIS data hold great potential for estimating NPP with the CASA model because of the appropriateness of both its spatial and its spectral resolution.     

Envisat ASAR的区域森林-非森林制图

Envisat卫星ASAR传感器的双极化数据对区域森林监测十分有效。通过分别采用SRTM DEM和Landsat TM图像对地形起伏区域和平坦区域的SAR图像进行地理编码,发展了一种SAR图像自动预处理方法。基于冬季单时相ASAR数据的HH(水平发射,水平接收)、HV(水平发射,垂直接收)极化比值和HV极化图像,提出了一种面向对象的森林-非森林分类方法。将之应用于中国东北森林/非森林制图,分类总体精度、森林用户精度和生产者精度分别为83.7%,85.6%和75.7%。结果表明,本文提出的方法十分适合区域森林-非森林制图的业务化运行。     

Potential of SARAL/AltiKa for Inland Water Applications

SARAL uses the same orbit as ERS and Envisat and can be used to extend inland water height time series derived from these missions. This article investigates the potential of SARAL for this application over the Great Lakes and the Amazon basin. SARAL/AltiKa is the first altimeter using Ka-band that is rarely influenced by ionospheric effects but susceptible for atmospheric water. Our investigations show clear waveform disruptions for SARAL due to precipitation. It is demonstrated that the quality of water heights improved when using alternative retracker products, for example, the ice-1 product. The improvement depends on the weather and yields up to 3.8 cm for wet conditions. The advantage of the smaller footprint of SARAL is demonstrated for land-water transitions where SARAL provides better water level heights up to 6 km to the lakeshore whereas Envisat is limited to about 11 km. SARAL provides also more reliable water level heights for narrow Amazon rivers than Envisat. Furthermore, the hooking effect is decreased for SARAL. Comparing water level time series of SARAL-only, Envisat-only, and multi-mission with in-situ data demonstrates that SARAL has the potential to extend Envisat long-term time series and to decrease the RMS by about 10% for large lakes and 40% for selected rivers.