Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.     

A 10-year wave energy resource assessment and trends of Indonesia based on satellite observations

Wave energy resource assessment and trends around Indonesian's ocean has been carried out by means of analyzing satellite observations. Wave energy flux or wave power can be approximated using parameterized sea states derived from satellite data. Unfortunately, only some surface parameters can be measured from remote sensing satellites, for example for ocean surface waves: significant wave height. Others, like peak wave period and energy period are not available, but can instead be estimated using empirical models. The results have been assessed by meteorological season. The assessment shows clearly where and when the wave power resource is promising around Indonesian's ocean. The most striking result was found from June to August, in which about 30–40 kW/m(the 90 th percentile: 40–60 kW/m, the 99th percentile: 50–70 kW/m) wave power energy on average has been found around south of the Java Island. The significant trends of wave energy at the 95% level have also been studied and it is found that the trends only occurred for the extreme cases, which is the 99th percentile(i.e.,highest 1%). Wave power energy could increase up to 150 W/m per year. The significant wave heights and wave power have been compared with the results obtained from global wave model hindcast carried out by wave model WAVEWATCH III. The comparisons indicated excellent agreements.     

MERIS遥感数据特性及应用

中等分辨率成像频谱仪(M ER IS)是搭载于由欧洲空间局(ESA)发射的迄今为止最大的综合性环境卫星ENV ISAT-1上的主要传感器,由法国和荷兰共同研制,是目前水色传感器中最有优势的传感器之一。文中主要介绍了M ER IS遥感数据的特点,与M OD IS、SeaW IFS传感器相比,M ER IS在波段设置与辐射灵敏度等方面都有较大的优势,更适合于海洋水色的应用。针对M ER IS的波段设置,重点介绍了M ER IS数据在海洋水色的应用范围与应用原理,并简要介绍其在大气与陆地上的应用。     

基于主成分分析的ENVISAT ASAR数据在福建省漳州市香蕉园地提取中的应用(英文)

Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multi-temporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier. Supported by the Program for New Century Excellent Talents in University (NCET-05-0573), Fujian Science and Technology Project (No.2006I0018), the Science Project of the Education Department of Fujian Province(No. 2006F5022).     

基于PSInSAR方法和ASAR数据监测天津地面沉降的试验研究

概述了PSInSAR方法的技术流程，阐述了定标ENVISAT ASAR数据对PS候选点（Persistent Scatterer Cadidate，PSC）筛选 的作用。研究表明：对ENVISAR ASAR 数据进行定标有助于筛选出更多的PS候选点；以定标得到的后向散射系数作为阈值，可以剔 除散射信号统计特性较稳定但散射强度低的像素点，从而避免可能由这些点引入的相位误差。在初步实现PSInSAR方法的基础上， 运用14景ENVISAT ASAR数据获得了天津地区的年均线性沉降速率，揭示的天津市地面沉降趋势与前人研究结果较为一致，获得的形 变速率值的准确性尚待数据量的增多而进一步提高。     

多源遥感数据反演土壤水分方法

基于ASAR-APP影像数据和光学影像数据,根据水云模型研究了小麦覆盖下地表土壤含水量的反演方法。利用TM和MODIS影像构建的植被生物、物理参数与实测小麦含水量进行回归分析,发现TM影像提取的归一化水分指数(NDWI)反演精度较好,相关系数达到0.87。根据这一关系,结合水云模型并联立裸露地表土壤湿度反演模型,建立了基于多源遥感数据的土壤含水量反演模型和参数统一求解方案。反演结果表明:该方案可得到理想的土壤水分反演精度,并可控制参数估计的误差。反演土壤含水量和准同步实测数据的相关系数为0.9,均方根误差为3.83%。在此基础上,分析了模型参数的敏感性,并制作了研究区土壤缺水量分布图。     

基于数据融合的浅层地下水提取技术研究

为提高浅层地下水遥感探测的准确性和效率,开辟遥感找水新思路,首次将空间分辨率为30 m的Landsat-7ETM多光谱数据和空间分辨率为150 m(Wide Swath模式)的Envisat-1 ASAR先进合成孔径雷达数据进行了融合,并利用基于主成分变换(PCA)和小波变换(WT)的融合算法成功地找到了浅层地下水信息异常带。通过实地调查和物探、钻探验证,I、II、III级找水靶区的富水性情况与预测结果基本一致,并在上述靶区找到了丰富的浅层地下水,证实了该方法的可行性和实用性,可为今后地下水快速勘察提供新的技术手段。     

基于多时相多极化差值图的稻田识别研究

提出了一种基于多时相多极化差值图的稻田识别方法,该方法在简化稻田识别算法的同时,仍具有较好的稻田识别精度.以江西省高安地区的早稻识别为例,利用两景ENVISA ASAR交叉极化模式数据(VV/HH)计算了同时相多极化差值图和同极化多时相差值图.由于稻田含有水层和水稻的垂直株型等属性特征,稻田在两时相上VV极化和HH极化后向散射差异都很大,且与其他地物具有明显差别,因此利用同时相多极化差值图可以很好地分辨出稻田来;从时间变化看,HH极化雷达波对水稻生长和稻田的变化比对其他地物的变化更敏感,使稻田分布信息在HH极化多时相差值图中反应突出.而VV极化对地物的时相变化不够敏感.因此,建立最优差值图组合,分别采用阈值分类方法和监督分类方法对差值图组合进行分类提取稻田.通过比较分类结果,认为基于统计分析的监督分类方法更好,其稻田识别的精度达到84.92%.文章最后对提出的稻田识别方法及分类结果进行了分析.     

ENVISAT测高数据波形重跟踪分析研究

对卫星测高技术中的波形重跟踪技术及其算法进行了详细分析讨论．同时分析了波形重跟踪中最小二乘解初值及权的确定,根据ENVISAT的SGDR波形数据,利用波形重跟踪技术研究了开阔海域上的距离改正情况(一般距离改正为20～30cm)。结果表明,重跟踪改正后的精度,18Hz距离优于1Hz距离。     

利用MERIS水汽数据改正ASAR干涉图中的大气影响

大气对流层对雷达信号的传播延迟是制约重复轨道InSAR高精度测量应用的重要因素之一.本文描述了MERIS水汽数据用于ASAR干涉图大气改正的方法;并以美国南加州地区为例,选取4对ENVISAT ASAR数据进行了大气改正的研究.结果显示对这4幅干涉图,经过MERIS水汽数据改正后InSAR与GPS差异的RMS分别〖JP2〗降低了41.7%,65.2%,19.3%和39.4%.平均改善程度达41.4%.更重要的是,经过MERIS水汽改正后,从2005~2007年〖JP〗干涉图和2004~2007年干涉图中,能清楚地识别出三处形变最明显的区域：Long Beach-Santa Ana 盆地、Pomona-Ontario和San Bernardino,其形变速率从-8 mm/a到-28 mm/a,大部分在-20 mm/a左右,与这些地区2003年以前的历史形变速率基本一致.因此,采用无云条件下的MERIS水蒸汽数据改正同步获取的ASAR干涉图,可以显著地降低大气水汽对干涉图相位的影响,从而更真实地反映地表形变等地球物理信号.