黑河实验区地表净辐射区域分布及季节变化

利用卫星遥感信息和地面观测资料，分析研究黑河实验区地表净辐射的区域分布及季节变化特征。结果表明，卫星遥感结合地面观测，首先可以得到较为精确的地表反射率和地表温度分布，进而得到较为合理的地表净辐射的区域分布和季节变化特征。     

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宁夏一次短时暴雨天气的跟踪预报分析

用卫星云图、雷达对2008年8月7日强对流天气产生的暴雨过程跟踪观测资料,结合自动站地面降水实况和WRF模式风场资料进行短时预报分析.结果表明:扩散东移冷空气和副热带高压边缘偏南暖湿气流的共同影响下,大气层结不稳定发生强烈的强对流天气;利用卫星云图,新一代天气雷达基本反射率强度图、0.5°仰角基本速度图和风廓线产品跟踪监测,综合分析预报短时暴雨天气是有效的方法.     

由ERS-1波形重构确定我国近海平均海平面

近海岸区域平均海面高在大地测量学、物理海洋学以及地球物理学研究中具有非常重要的意义.受各种条件的制约和限制,目前卫星测高技术主要应用于深海区域,在近海区域尤其是海岸线附近区域的应用几乎为空白.本文根据ERS-1测高卫星回波波形特征,采用五参数线性模型,由最小二乘拟合方法,对近海区域尤其是靠近海岸线附近的ERS-1测高波形数据进行波形重构.比较波形重构前、后解算平均海面高,表明波形重构技术不仅明显改善了解算近海海面高的精度,而且增加了近海测高海平面的分辨率,并使卫星测高有效观测延伸至海岸线附近.随后,本文利用波形重构后海面高数据构造了近海多年平均海平面,并对我国近海海平面特征进行了初步分析.     

基于静止卫星红外云图的MCS普查研究进展及标准修订

基于静止卫星红外云图的MCS普查标准不统一不利于各种MCS普查结果的对比分析, 该文在总结MCS普查研究进展的基础上, 依据Orlanski尺度分类标准对MβCS普查的最小尺度标准作了修订, 即修订为TBB≤-32 ℃的连续冷云区直径≥20 km。根据马禹等的MβCS普查标准和该文修订的MβCS普查标准, 利用GOES-9卫星红外云图普查了2003年6月19日—7月22日淮河大水期间的MCS, 结果发现共有10个MαCS和24个MβCS, 并对24个MβCS作了普查标准修订前后的统计结果对比, 发现新的普查标准比根据马禹等的MβCS普查标准获得的结果多7个MβCS, 并且这7个MβCS中有6个都引起强降水, 因此这种对比分析结果表明:新MβCS普查标准对揭示淮河大水和MCS的关系更具合理性。此外, 还分析了3个因MβCS而引起局地强降水的典型个例, 这些MβCS的直径尺度只有几十至一百多公里, 不符合马禹等的MβCS普查标准。对这3个MβCS分析结果表明:该文新MβCS普查标准有助于对产生剧烈天气的MCS的普查研究和预报。     

用三通道合成彩色图像进行云的分类解释判读

为更好地利用FY-3A气象卫星上中分辨率光谱成像仪(MERSI)资料高空间分辨率及多光谱的优势,细致分析云系在宏、微观方面的多重特征,首先利用平面平行辐射传输模式(SBDART)证明了MERSI的0.65,1.6 μm和11.25 μm通道能够分别反映云光学厚度、云粒子大小、云顶高度的信息,然后采用三通道合成彩色图像的...     

利用物理方法确定外方位元素的卫星立体摄影测量的精度分析

本文在分析用物理方法确定外方位元素的情况下卫星立体摄影测量的精度影响因素,推导了卫星立体摄影测量的理论精度计算公式,并以SPOT5 HRS立体像对为例,计算了SPOT5 HRS立体测量的理论精度,进行了利用少量地面控制点的立体测量试验。结果证明卫星立体摄影测量的理论精度估算公式是正确的,同时SPOT5 HRS立体测量精度能够满足1∶50000测图的要求。     

Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project

Within the SLAM project (Service for Landslide Monitoring), launched in 2003 by the European Space Agency (ESA) the Permanent Scatterers (PS) technique, a multi-image interferometric approach, coupled with the interpretation of aerial-photos and optical satellite images, was carried out for landslide investigations. The PS analysis was applied at a regional scale as support for landslide inventory mapping and at local scale for the monitoring of single well-known slope movements. For the integration of the PS measurements within a landslide inventory the Arno river basin (Italy) was chosen as test site for the presence of a high number of mass movements (to date about 300 areas at high landslide risk and more than 27,000 individual landslides mapped by the institutional authorities). About 350 SAR images have been interferometrically processed by means of the PS technique, with the detection of about 600,000 PS. The use of optical images contributed spatial meaning to the point-wise information provided by the PS, making it easier to identify terrain features related to slope instability and the landslide boundaries. Here we describe the employed methodology and its impact in the updating of a preexisting landslide inventory. 6.8% of the total number of landslides were characterized by ground displacement measurements from the PS: 6.1% of already mapped landslides and 0.8% of new unstable areas detected through the PS analysis. Moreover, most of the PS are located in urban areas, showing that the proposed methodology is suitable for landslide mapping in areas with a quite high density of urbanization, but that over vegetated areas it still suffers from the limitations induced by the current space-borne SAR missions (e.g. temporal de-correlation). On the other hand, the use of InSAR for the monitoring of single slow landslides threatening built-up areas has provided satisfactory results, allowing the measurement of superficial deformations with high accuracy on the landslide sectors characterized by a good radar reflectivity and coherence.     

Sea Level Prediction in the Yellow Sea From Satellite Altimetry With a Combined Least Squares-Neural Network Approach

Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast.     

用三通道合成彩色图像进行云的分类解释判读

为更好地利用FY-3A气象卫星上中分辨率光谱成像仪 (MERSI) 资料高空间分辨率及多光谱的优势,细致分析云系在宏、微观方面的多重特征,首先利用平面平行辐射传输模式 (SBDART) 证明了MERSI的0.65,1.6 μm和11.25 μm通道能够分别反映云光学厚度、云粒子大小、云顶高度的信息,然后采用三通道合成彩色图像的方法,对FY-3A气象卫星云图进行云的分类解释判读。该技术可直观区分有云区、无云区,显示海陆分界,并且使不同云类在云图上体现为不同颜色。同时,FY-3A气象卫星高达1000 m甚至250 m的空间分辨率也使云的细致结构更为清晰,两项优势的叠加,大大提升了云的分类解释判读的准确程度和精细化水平。文中还尝试寻找典型云系的三通道特征值,以期为模糊C均值聚类 (FCM) 方法中聚类中心的选定提供经验值参考。