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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Spectral Downscaling of Integrated Water Vapor Fields From Satellite Infrared Observations
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Spectral Downscaling of Integrated Water Vapor Fields From Satellite Infrared Observations

机译：从卫星红外观测到的综合水蒸气场的光谱缩减

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摘要

Atmospheric water vapor is a crucial constituent affecting both climate change and hydrological cycle processes, whereas on the other hand, it has a significant impact on the electromagnetic signal propagation. Since the distribution of atmospheric water vapor strongly varies with time, location, and altitude, it is necessary to monitor it at high spatial and temporal resolution. Unfortunately, mapping its spatial distribution is difficult due to the lack of meteorological instrumentation at an adequate spatial and temporal observation scale. For many geophysical applications, there is also the need to reconstruct spatial details of integrated precipitable water vapor from information available only at coarser spatial scales. Spatial downscaling approaches can play a significant role when high-resolution water vapor retrievals from relatively new sensors, like synthetic aperture radars, or from conventional sensors, like the infrared radiometers MEdium Resolution Imaging Spectrometer (MERIS) or Moderate Resolution Imaging Spectroradiometer (MODIS), are used in synergy to enhance the accuracy of integrated water vapor retrievals. In this context, this paper introduces some new methodological aspects to increase the spatial resolution of integrated precipitable water vapor observations using a statistical downscaling spectral approach. To highlight the potential and the usefulness of the proposed downscaling estimation procedure, collocated 250-m MERIS and 1-km MODIS acquisitions are used. Results reveal the ability of spectral downscaling to reproduce quite well the second-order statistical variability of the water vapor field at small spatial scales with a root-mean-square error comparable with conventional interpolation techniques.
机译：大气水蒸气是影响气候变化和水文循环过程的重要组成部分，而另一方面，它对电磁信号的传播有重大影响。由于大气中水蒸气的分布随时间，位置和海拔高度而变化很大，因此有必要以高时空分辨率对其进行监视。不幸的是，由于缺乏足够的时空观测尺度的气象仪器，很难绘制其空间分布图。对于许多地球物理应用，还需要根据仅在较粗的空间尺度上可获得的信息来重建可沉淀水蒸气的空间细节。当从相对较新的传感器（如合成孔径雷达）或从常规传感器（如红外辐射计，中分辨率成像光谱仪（MERIS）或中分辨率成像光谱仪（MODIS））中高分辨率提取水汽时，空间缩减方法将发挥重要作用，用于协同作用以提高综合水蒸气回收的准确性。在此背景下，本文介绍了一些新的方法论方面，以使用统计缩减光谱方法来提高可沉淀水汽综合观测的空间分辨率。为了突出提出的缩减估算程序的潜力和实用性，使用了并置的250米MERIS和1公里MODIS采集。结果表明，光谱缩小的能力可以很好地再现水蒸气场在较小空间尺度上的二阶统计变异性，其均方根误差可与常规插值技术相比。

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