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Comparison of Satellite-Derived Precipitable Water Vapor Through Near-Infrared Remote Sensing Channels

机译:通过近红外遥感通道获取的卫星可沉淀水蒸气的比较

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The retrieval accuracies of three typical near-infrared (NIR) precipitable water vapor (PWV) products are thoroughly discussed in this article. The NIR PWV data are obtained from three satellite sensors: the Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra, the medium-resolution imaging spectrometer (MERIS)/Envisat, and the medium-resolution spectral imager (MERSI)/FY-3A. Collocated Global Positioning System (GPS) PWV data from GPS network are employed as the reference data set because of its high precision in water vapor measurement. Relative difference and root-mean-square (rms) difference are computed for "Clear," "Cloudy," and "All Weather" categories for each NIR water vapor product. The results reveal that PWV derived from NIR sensors tend to underestimate the water vapor values with the existence of cloud, as NIR signals cannot penetrate the cloud. Under "Clear" condition, the overall rms for remote sensors are close to the expected goal accuracies, namely, with root-mean-square-error (RMSE) of 5.480 mm for MODIS/Terra, 3.708 mm for MERIS/Envisat, 8.644 mm for MERSI/FY-3A. MERIS/Envisat has the highest PWV retrieval accuracy, while the MODIS/Terra PWV product has the best correlation with GPS PWV (R-2 is 0.951). The MODIS/Terra tends to overestimate PWV value, while MERSI/FY-3A tends to underestimate the PWV value. Moreover, a comprehensive comparison of seasonal variation and wet/dry variation for each NIR PWV product is also performed in this study. The results indicate that the RMSE increases significantly under wet conditions (PWV larger than 20 mm) than under dry conditions (PWV smaller than 20 mm) for all remote sensing PWV products.
机译:本文将详细讨论三种典型的近红外(NIR)可沉淀水蒸气(PWV)产品的检索精度。 NIR PWV数据是从三个卫星传感器获得的:中分辨率成像光谱仪(MODIS)/ Terra,中分辨率成像光谱仪(MERIS)/ Envisat和中分辨率光谱成像仪(MERSI)/ FY-3A。来自GPS网络的并置全球定位系统(GPS)PWV数据由于其在水蒸气测量中的高精度而被用作参考数据集。计算每个NIR水蒸气产品的“清除”,“阴天”和“全天候”类别的相对差异和均方根(rms)差异。结果表明,由于NIR信号无法穿透云层,因此从NIR传感器获得的PWV倾向于低估了云的存在下的水汽值。在“清除”条件下,远程传感器的总均方根值接近预期的目标精度,即MODIS / Terra的均方根误差(RMSE)为5.480毫米,MERIS / Envisat的均方根误差为3.708毫米,8.644毫米适用于MERSI / FY-3A。 MERIS / Envisat的PWV检索精度最高,而MODIS / Terra PWV产品与GPS PWV的相关性最好(R-2为0.951)。 MODIS / Terra倾向于高估PWV值,而MERSI / FY-3A倾向于低估PWV值。此外,本研究还对每种NIR PWV产品的季节变化和干/湿变化进行了全面比较。结果表明,对于所有遥感PWV产品,在潮湿条件下(PWV大于20毫米),RMSE显着高于干燥条件下(PWV小于20毫米)。

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